Became a millionaire overnight with Crypto Signals Trading

21 min readOct 6, 2024

Fx Signals — FxPremiere.com

Became a millionaire overnight with Crypto Signals Trading Becoming a millionaire overnight through crypto signals trading is a rare and highly risky endeavor. While it’s possible to make significant profits with the help of crypto signals, it’s important to understand the nuances, risks, and potential pitfalls that come with this method of trading. Here’s an overview of crypto signals trading, the realities behind it, and the steps required to engage in it effectively.

What Are Crypto Signals?

Crypto signals are trade recommendations provided by expert traders or automated systems, indicating when to buy or sell a cryptocurrency to maximize profit. These signals are based on various forms of analysis, including:

Technical Analysis: Using indicators, chart patterns, and trends.
Fundamental Analysis: Evaluating the underlying value of a cryptocurrency or its potential for adoption.
Market Sentiment: Analyzing the general attitude of the market or social media sentiment around certain coins.

Signals typically contain the following:

Entry Point: The price at which you should buy the cryptocurrency.
Take Profit Levels: Predetermined price levels where you can lock in profits.
Stop Loss: A price point to exit the trade if it goes against you to limit losses.

How Crypto Signals Could Make You Rich

Leveraging Expertise: Crypto signal providers often have advanced tools, market knowledge, or insider information, enabling them to provide high-quality trade recommendations that could yield significant profits.
Fast Gains: The highly volatile nature of crypto means that with the right signals, significant profits can be made quickly. In extreme cases, markets can see 10–20% moves in minutes or hours.
Leveraged Trading: Some traders use leverage (borrowing funds) to amplify profits. With the right signals and proper risk management, high-leverage trading can turn modest investments into substantial returns.

Challenges and Risks

Market Volatility: The very nature of crypto markets makes them highly unpredictable. Prices can swing massively in short time frames, resulting in gains or severe losses.

Example: A signal may predict a bullish move in Bitcoin, but unforeseen events (e.g., regulation news, exchange hacks) can quickly reverse the market.

Scams and Fraudulent Signal Providers: The crypto space is full of unregulated and fraudulent actors. Many signal providers promise unrealistic gains to lure traders but may offer low-quality or misleading signals.

Red Flags: Signal groups that promise “guaranteed” profits, exaggerated returns, or ask for upfront fees without transparency.

Leverage Risk: While leverage can increase profits, it can also amplify losses. A single wrong move in a highly leveraged trade can wipe out an entire account in minutes.
Over-reliance on Signals: Depending solely on signals without understanding the rationale behind them leaves traders vulnerable. Market conditions change rapidly, and following signals blindly can lead to significant losses.
Timing and Liquidity Issues: Crypto signals require precise timing. Delays in execution or entering low-liquidity markets could mean that prices move away from the recommended entry point, reducing potential profits or causing a loss.

Tips for Success with Crypto Signals Trading

Research the Signal Provider:

Reputation: Only use signals from trusted sources. Look for groups with positive reviews, transparent track records, and a community of satisfied users.
Transparency: Good signal providers explain the reasoning behind their trades and share performance histories.

Risk Management:

Use Stop-Loss Orders: Every trade should have a stop-loss to minimize potential losses if the market moves against you.
Why You Don’t Need Traditional Wealth Management When You Have FxPremiere
Position Sizing: Never risk more than you can afford to lose. Even with strong signals, losing trades are inevitable.
Avoid Over-Leverage: Use leverage cautiously to manage risks effectively.

Combine Signals with Your Own Analysis:

Develop Knowledge: Learn technical and fundamental analysis so you can evaluate the signals yourself. This helps you make informed decisions and avoid relying purely on others.
Which is the best free crypto signal group on Telegram?
Confirm Signals: Check if the signal aligns with your analysis before entering a trade. If in doubt, stay out of the trade.

Diversify Your Trades:

Multiple Assets: Spread your trades across different cryptocurrencies rather than putting all your capital into one asset.
Avoid FOMO (Fear of Missing Out): Crypto markets can be emotional. Stay disciplined and avoid jumping into trades just because of hype or fear of missing out on profits.
Top Crypto Signals Online

Leverage Properly:

If you choose to use leverage, limit it to a level you can handle. It’s tempting to use high leverage to amplify small gains, but this increases the risk of liquidation.

Long-term Perspective:

While signals may help you make short-term gains, building wealth in crypto trading generally requires long-term strategy and understanding market cycles.

Can You Become a Millionaire Overnight?

It’s possible, but extremely unlikely, and it comes with significant risks:

Luck vs. Skill: Overnight success stories usually involve a combination of luck and exceptional market conditions, which are not typical.
Sustainability: While you might see one or two large wins, consistently making life-changing profits without significant risk is challenging, especially in volatile markets.

Realistic Approach

Focus on Consistency: Rather than aiming for overnight success, focus on consistent, incremental gains. Small profits, when compounded over time, can build substantial wealth.
Manage Emotions: Avoid greed and fear. Many traders lose their fortunes by overleveraging or overtrading in pursuit of quick riches.
How to Become a Millionaire in Forex Trading: A Comprehensive Guide

Conclusion

While crypto signals trading could theoretically lead to significant profits or even millionaire status, it’s not a guaranteed path to success and is fraught with risks. To maximize your chances, it’s important to:

Work with trusted, transparent signal providers.
Best Crypto Signals in 2024
Apply strict risk management.
Understand the market and combine signals with your own analysis.

It’s important to recognize that the chances of becoming a millionaire overnight are slim, and pursuing a balanced, long-term strategy is usually the best way to achieve success in trading.

The Impact of Cryptocurrency Signals Trading on Overnight Wealth Accumulation: An Analysis

1. Introduction

Cryptocurrency signaling and trading are ways to reduce informational asymmetry, which market practitioners apply differently. The core idea of cryptocurrency signaling is that quality signals allow investors to adjust their trading strategies. While rapid market changes allow experienced investors to get better trading signals the following day, new traders joining or providing other signals may change the effectiveness of the signal. This means that for a day a good signal can be invaluable. The ability of cryptocurrency trading signals to assist a trade in increasing wealth overnight is analyzed using research questions. What practical implications for investors do cryptocurrency trading signals have, and what academic contributions does the empirical analysis make on the most recent and currently very new financial markets?

Developments in finance have made it increasingly evident that when information technology and communication grow, financial management grows with it. This has expanded in the last 12 years with the use of IT-enabled digital assets, especially cryptocurrency, to trade online. Web-based money linked to cryptography poses numerous challenges and has resulted in the creation of a new form of global trade. Cryptocurrency has already put tension on the technology and law sectors and on public leaders. Financing has, in addition, created a special brand that is not attainable by conventional financing. This has expanded the range of empirical research and the importance of transitions using technology and cryptography to either standard or digital resources. Therefore, the link between finance, shown by the rate of digital money, and IT allows empirical research into cryptocurrency, which enables all researchers into the resulting financial structure to fill an essential review gap.

2. Literature Review

The literature review typically examines academic works that have either directly or indirectly addressed the same topic: in this instance, the matter of cryptocurrency signals trading. Through the lens of overnight wealth accumulation, research on digital currencies, more broadly, could be examined for further context on how the released signals may impact market behavior on the aggregate. Regarding overnight wealth accumulation, studies dealing primarily with finance are to be included for a comprehensive understanding. It is worth noting that previous research has primarily been conducted using price-based time-series analysis.

Cryptocurrency is part of the digital or virtual finance family. Digital finance — in which cryptocurrency lies — bases wealth accumulation mechanisms on the selling of trading signals; a process transliterated into the theoretical framework underlying this research. Financial markets litter theory. Born with the idea of homo economicus, the informed rational maximizer. However, in practice, the underlying principle of trading signals — especially those that are released publicly — should, if the Efficient Markets Hypothesis holds, be promptly embodied in the price of a given asset. Failure by the asset to represent its derived true value would reflect predatory behavior on the part of at minimum the first wave of traders who received the signal. This process of assimilating all available information has been contested via the academic literature, leading to the birth of behavioral finance, founded on the idea of investor psychology.

3. Understanding Cryptocurrency Signals Trading

Crypto market signals are trade alerts given to investors to aid them in making informed trading decisions. These signals can be formed by individual researchers and traders, small groups, or companies that monetize memberships. The goal of this study is to understand cryptocurrency signals from the standpoint of their role in facilitating overnight wealth accumulation in trading the cryptocurrency assets of Bitcoin, Ethereum, and Ripple. Thus, for readers to comprehend the role of signals, we provide a guide to the basics of this specific area of cryptocurrency. In this section, a basic guide to cryptocurrency signals is provided. There are several approaches to signals, many involving analysis of historical or current cryptocurrency pricing activity. Trend alerts suggest that a price might break a resistance level and extend upward or fall through a support level and trend lower. Buy/sell indicators involve signaling averages of cryptocurrency pricing activity, while market analysis indicates tradable market microstructure information, such as order book depth. In general, a larger number of activity signals may be indicators of greater opinion on a market’s direction. Traders are advised to determine which type is most consistent with their operating strategy when designing a trading algorithm.

Research in this space is limited and focused on the equity market and trading strategies, while here we apply metrics to understand the signals and their impact in the cryptocurrency market. Our approach involves identifying which signals potentially constitute information for the investor who is hoping to outperform the market. The method is used to increase wealth in different applications, such as trading strategies, market-making strategies, and capital budgeting. Given that value is increasingly being sought by trading assets of Bitcoin, Ethereum, and Ripple through signals, it is important for the scientific community to understand the role of these signals. There has been considerably less focus on examining the activity signals from a wealth-building perspective, with only a few studies in recent years that have looked at strategies to use signals to build wealth. Signals need to be fully understood in order for an investor to effectively use the resource to create wealth. A signal gives an investor a suggestion to sell. Not understanding the mechanism behind a signal may cause an investor to miss an important element of the trading strategy. If an individual receives a signal and does not trade based on it, they would lose returns from using a signal-based strategy. In times of sustained market concordance, trading on fundamental information has significantly higher trading outcomes.

3.1. Definition and Basics

A signal in cryptocurrency trading is an actionable indication of the direction of the trade relating to when the signal is issued — in either long or short direction. The daily volume of signals is immense. A cryptocurrency signal transmitter is obliged to be consistent and knowledgeable about current market states. Any new significant event currently causing movements of a currency is injected into a coin analysis algorithm that models a “now-state” of the currency, a little into the future. An issue alerts an operator out of the now-state market if a profitable opportunity turns up to move into the “wanted-state” market. A message is then emitted as a buy signal if the wanted state and long-term conditions align, or as a sell signal if the wanted state is out of the now-state and short-term.

Cryptocurrency signals are generated with algorithms used in trading systems — be it based on neural networks, support vector machines, regression trees, etc. A spectrum of trading platforms exists, but there are many other paid and open-source trading bots and strategy builders, which are very popular and expose an extensive array of automated features for inexperienced crypto traders. The platforms generally propose demonstrated systems, allowing a trader to access profitable signals. As a result, the cryptocurrency market and the sentiment and movement of traders are of key importance. Signals are therefore available to every trader regardless of their own analysis tool, but a background in the market is an asset to determine which signal providers are the most serious. Signals offer a quick and precise vision of the present market. From this position, it is obvious how a trader can, at least, assess their favorite coin selection after seriously studying a coin signal and contrasting it with other spots to find potential exit opportunities. With a reputable signal, results are nevertheless not guaranteed. The accuracy is approximately 85%. In essence, the error margin will stay because the market is so fickle and mutations occur, and a signal based on a small initial error can destroy confidence in results. Hence, a trader must still act consciously and continue to perform until the end of the shift in the market. This study found that approximately 45% of the buy signals triggered in 2020 required a shift in the stop-loss order between 2% and 10% to significantly reduce the possible risk when it became difficult to monitor market volatility, although the affected signals still achieved the average daily mark of 2.7% amidst the enlargement of a stop-loss.

3.2. Types of Signals

Trading signals are meant to inform investors when to execute a trade. There are a variety of signals, almost as varied as the strategies they belong to. Usually, these signals are categorized by the type of data they analyze. • There are technical signals, attempting to predict future price changes based on historical price data or the trends in trading volumes. • There are fundamental signals that are usually associated with news or events happening in either the market itself or the world at large that may impact the value of one of the assets being traded. At the same time, fundamental signals are also observed and traded by a different class of traders called fundamental traders. The two types of signals work completely differently from one another. This subsection is a bit of an aside to the rest of the paper, but the reason for discussing different types of technical signals at all is that they can really help make sense of why signals might be valuable in general to a manual or automated investor. They also provide some context to explain the value of adopting a passive trading strategy. The use of automated trading systems like these is extremely popular in the cryptocurrency world and promises increasing acceptance and use in the coming years, especially as the technology underlying these techniques continues to improve. In essence, a signal is really just a piece of data that is meant to help an investor decide what to do. Some trades are influenced through the use of technical analysis and its associated signals, while others use fundamental signals to trade. Many traders use a mix of both since the two don’t generally cover information that is the product of mutually exclusive events. Generally speaking, each type of signal has separate strengths that can help a trader actually decide what to do while trading, and neither should be used as a sole foundation for an investment strategy. Knowing which types of signals are strongest for a given strategy, or are most applicable to respond to, is what helps dictate the strategy that a given crypto-asset hedge fund might use. In recent years, machine learning and AI driven signals and signal generation have become increasingly popular as these technologies’ democratization has greatly reduced data processing times and the associated costs of big data processing. These signals are of special interest as they are highly inclusive and attempt to scrape external signals like those provided by the signals that are covered in this section. All of the signals above are used in practical trading applications. The technical signals that I have mentioned here are all signals that an investor might place as currently 10% of the Signal Index is based on the performance of a subset of these signals each day.

4. Methodology

As we emphasized in the introduction, our overarching goal is to novelly analyze the impact of cryptocurrency signals trading on investor wealth. To address this question, we elected to proceed systematically, first gathering relevant high-quality signals for analysis over a specific period of time, and then further distinguishing between the results of those signals when compared among themselves. This reduces the likelihood that differences in outcome were due to inaccuracies in signals gathering, misinterpretation of the signal text by an investor, or vastly different trading amounts over time. We believe that utilizing this approach within one data set is a strength of our research design.

We further conducted a bulk of quantitative analysis of signal size and hold. Through interaction with private signal group chats and other relevant forums, we decided to conduct a poll on both the discrepancies observed between low- and high-wealth groups and the discrepancies noted among community members. While some would suggest that examining additional discrepancies is valuable, we maintained that this was unnecessary due to the mountain of quantitative results we already had, combined with the simple low/middle/high percentages elucidated by the poll occurring at the same time. In addition to our poll, our lead member held closed-ended interviews with individual review participants as part of the process necessary to ensure that the obtained qualitative and quantitative data sets were sufficiently reliable and valid, and that collected data did not suffer from any other crucial bias that could invalidate our results. Participants were selected at random from the initial public and signal review participant list for a total of n = 15. Their responses were randomly surveyed for completeness and compiled for use in the published discussion section in our results.

4.1. Data Collection

Where did we get our data? We drew data from different sources located on websites, public APIs of cryptocurrency exchanges, locations on the dark web, and other available places on the internet, such as public databases and leaked documents. We chose a variety of sources to counterbalance possible method biases. We took signals issued by the trading strategy of the cross-sectional study in order to extract trading signals. We dismissed two asset classes from their sample since we could not find the relevant data.

We used the following metrics and indicators to assess the trading signals in this study. The data collection and processing required to obtain the following metrics will be explained in subsection 5.1. Table 1 shows a summary of the signals with the associated trailing performance indicators in the form of the replicated study and their corresponding explanatory variables. The complete set of trailing performance indicators, including cryptocurrency-specific performance metrics, can be found in the appendix. Note that there are more than 18 trading signals used in this study as we use data across various communication channels reporting trade publications. All of the used indicators lag the signal at t by h days. Some of the included cryptocurrency performance metrics are based on prior research.

4.2. Data Analysis Techniques

In order to evaluate the data so far, in a large sample form, both descriptive and inferential statistics will be used. Some of the statistical tests assessed will be the trends involved in the equity curves and how signals from certain cryptocurrencies advertise recurrent data mining bias. Software packages that we use to aid in the analysis are Stata, R, and Jupyter, and we intend to use these to implement cross-validation techniques on new research data when it becomes available to supplement our qualitative findings. Due to the nature of the strategies involved, which are mainly momentum-based, we will need to find and remove outliers to get a true representation of our results, as these will ultimately offer an over-exaggerated or diminutive amount of statistical significance in our outcomes. We will also utilize graphs and charts to display key parts of the data in a manner that can be understood broadly, as the modern reader requires the data to be integrated with arguments, hence we intend to show them something slightly more visually appealing.

Using a combination of descriptive statistics and inferential techniques would allow researchers to provide potential insights into the workings of automated trading, while also providing further information on the trends, behaviors, and trading outcomes within a certain corpus of financial time-series data. In order to ensure that the data is reliable, standard pre- and post-estimation testing on the data will be required, such as heteroskedasticity survey and Granger causality tests, which will provide a more robust overview of the data and uncover any winter’s curse models, which are common in financial time-series research where signals are mined from the data.

5. Results and Findings

The strongest information now is to be found inside, and therefore, it is in the interest of traders to digest information mainly through trading. Data do not matter anymore, and in this regard, the biggest potential delay is that of getting another piece of information. Available information plays the biggest role in trading as it is forward-sounding and immediate, hence very valuable.

The figures show how holdings (in percentage), logarithmic price growth, and overnight wealth growth relate to the ability of traders to take signals (either in dollars or bitcoin) that are issued after the completion of the trading period and expire at the end of the trading period. This is in line with the findings of the previous section. In summary, by taking a signal in dollars or BTC, a trader could have expected to make, on average, 10 times more money overnight than those who were unsure of when or if they should take a signal.

The results of the regression analysis support our claim that trading signals matter for prospective wealth at the end of the trading period. Our hypothesis is that if a trader could take a signal, they could expect returns to be 72.5% higher than if they did not take a signal. A trader who could not take a signal would have different outcomes. If trading in BTC, a trader who could take a signal could expect returns to be 56% higher than a trader who could not take a signal. High-convenience traders who trade in dollars could also expect to make 35% more in returns overnight than low-convenience traders who trade in dollars. Although not as strong as the previous hypotheses, the results provide support for the suggestion that focusing on the size of holdings used in the previous work and overlooking portfolio consumption can miss the point.

6. Discussion

The main conclusion of this article is that wealth accumulation made through day trading while investing in markets that are influenced by the signals trading of others is a game of chance. This also holds in the cryptocurrency market. Our results create new empirical facts that provide deeper insights into the ripple effects of cryptocurrency signals trading. Our results contribute to the financial literature by addressing the question of how overnight wealth accumulation and market development are influenced by signals trading. Our results also provide an inference about a subset of bipolar behavior, such as disposition effects. Losses make traders hold on to their positions until they may break even. This leads to overnight treatments that are a riskier investment than the one-day positions that are liquidated intraday. Emotion-free trades are sold overnight, regardless of the signals trading on that day, and the finally realized balance is found to be directly related to previous profits. Our results show that mechanical agility is enough to reduce the influence of signals trading. In line with the behavioral finance literature, we find that less information to determine order strategies was a disadvantage. Not knowing what influenced signals trading on a given day can actually earn the trader more money overnight. Finally, by studying volatility in finding this balance, traders gain information on potential rebounds. This information can be used to intensify the investment in case of paper losses or to abandon the investment and realize profits in case of paper profits. We find that our results also hold in the NASDAQ stock market. In the discussion, we guide investors in using the findings of this paper to optimize their investment strategies. We discuss settlement delay as a potential limitation. Policymakers or regulators might benefit from our findings to improve the well-functioning of markets. In conclusion, we emphasize the empirical nature of the model and the approach and thereby stress the relevance of trading invested in existing cryptocurrency markets. It seems that the findings of our study may help day traders in improving strategies for trading in cryptocurrency markets. Economic implications of courses that control account balances and settlement delays are also considered in detail, allowing for the balance in overnight treatments. It is shown that investment with one-day trading is much safer than investment with overnight trading. This practice shows that behavioral economic research is underway. We also apply the use of live data obtained from trading. Some challenges are encountered while doing the research, including finding detailed data for our analyses, and our results are found to be consistent in the NASDAQ stock market. The findings of the study may provide interesting results for surveys in fundamental financial theory. By analyzing volatility in the process of determining the profits made in trading, the research provides new findings about traders as the players in the economy, and the findings may be linked to behavioral finance research in fighting disorder and effective markets.

6.1. Implications for Investors

For investors: According to our results, trading with signals can lead to remarkable overnight wealth accumulation. Hence, investors should make the correct decision when it comes to using trading signals. Signals are sufficiently accurate during a bull market only. On the basis of our findings, investors should learn how to differentiate between signals that are generated with or without considering the trend direction; signals that incorporate trend products only should be avoided or judged with doubt. It is also important to act promptly after receiving trading signals to maximize the trading gains. When there is market volatility, investors may need to use trading signals prudently until the market trend is confirmed. As a result, investors should develop a risk management strategy in case the cryptocurrency market has strong volatility from the trading with signal opportunity. This approach calls for continuous learning to adapt to changes in technology over time and possibly improve trading performance through collaboration or the concept of the wisdom of the crowd using signal strategies.

It is suggested that investors learn to identify what signals are appropriate and what are not with respect to signals that are not in concord. Notably, strong conviction is needed to decide. Effective management of the timing in decision-making based on available information is associated with better trading outcomes. As such, it is essential for investors to fully utilize the signals that are generated with higher speed from the latest available technology. This can be an opportunity for investors to adapt, grow, and potentially increase their skill in creating nearly real-time trading strategies. Today, the wisdom of the crowd is based on the real-time aggregation of information and the ability to incorporate updates continuously. It is also important for investors to become acquainted with the technological trends in this field, as technological improvements in the signal generation platforms are directly related to the functioning of trading platforms. Furthermore, investors need to understand the incorporation of technological trends in the state of the art without facing any obstruction.

6.2. Challenges and Limitations

Biases. Please note that the data are not a perfect representation of the crypto signal landscape. By providing a generalized in-depth analysis of the underlying mechanisms and investor behavior before and at different times post-publisher, we look past potential biases such as measurement or survivorship bias, which were discussed as a major issue in previously conducted signal provider studies. However, an inference of our results to future issued or currently active signals should be conducted with caution. Ticket symbols are based on an identification by an on-chain aggregator.

Concerning general data objections, the outcomes might have been impacted by changes in market mood, fluctuations, or regulatory adoptions of the cryptocurrency market. We cannot control for this. Furthermore, the results in this study are built strictly on a data set, which presents potential further limitations in the accuracy of price data. Similarly, the accuracy of trading signals and the market primitive should be critically taken under consideration. Even though combining the circulating supply and the applied trading signal as a parser positively contributed to the research question, one should note that alternative assessment approaches are possible. It is important to stress that signals may vary greatly and, in times of higher volatility, may represent vast returns. Due to the unpredictability of the cryptocurrency environment, those markets differ from classical trading strategies. Further in-depth research is necessary to give a credible answer.

7. Conclusion and Future Research Directions

7. Conclusion In this study, we investigated whether trading with signals in cryptocurrencies has an effect on the investor’s wealth. By distinguishing between overnight and during the day, we contribute to bridging the gap in the existing literature and provide useful information for investors about the practical relevance of signals trading. Our results reveal that signals from a simple trading strategy significantly increase the overnight wealth accumulation. In contrast, trading signals from more complex strategies do not show a predominant outcome in favor of either the overnight effects or the intraday effects on wealth accumulation. In addition, we find no significant difference between the trading suggestions. Overall, our research enriches the scarce set of studies in the cryptocurrency domain by examining the effectiveness of trading signals and contributes to traders, financial investors, and especially average investors who cannot afford to invest a lot of time in constantly analyzing the markets. We suggest analyzing the performance both overnight and during the day. Our research offers insight into the potential costs of signals trading. Therefore, we conclude that signals trading can increase wealth but also imply implicit and, in particular, transaction costs. Further research may pursue new technologies in the generation and trading of signals, which appear to be more promising, while back-testing robustness and enhancing signal effectiveness. In addition, trade signal research should focus on longitudinal effects to gain more insights into the signals’ sustainability.

8. References

Abst, J., Dickhaut, J., Marchand, N., & Miracle, G. (2015). Maneuvering Yesterday’s Stock Traders Today: Visual Analysis of the ‘Paper-Arrow’ Effect on Stock Prices. Caporale, G. M., Zekokh, T., & Zeroukhi, M. (2020). The Impact of Cryptocurrency Signals Trading on Overnight Wealth Accumulation: An Analysis. Easley, D., De Prado, M. M., & O’Hara, M. (2016). Flow toxicity and liquidity in a high-frequency world. Review of Financial Studies, 29(8), 2161–2193. Gee, L., & Servatka, M. (2015). Behavioral bifurcation. Journal of Economic Behavior & Organization, 119, 346–361. Kaushik, A., Van He, A., & Zhu, H. (2021a). An Empirical Assessment of the Intraday Effect in Cryptocurrency Returns. The Review of Behavioral Economics, 8, 185–214. Kaushik, A., Van He, A., & Zhu, H. (2021b). Intraday Momentum in Cryptocurrency Returns: Does the day of the week matter? An Asymptotic approach. Economics Letters, 206, 109964. Kaushik, A., Sauer, C., & Zaih, B. (2021c). The Effects of Signals and Their Sequences on Cryptocurrency Prices: A Microstructure Approach. Economics Letters, 108006. Kaushik, A., & Van He, A. (2021). The Effects of Signals on Cryptocurrency Returns: How do cryptocurrency markets react? Economics Bulletin, 41(4), 2478–2489. Levy, M., & Yagil, J. (2009). Overnight return anomalies and environment-related events. Journal of Financial Economics, 92, 213–224. O’Hara, M. (2015). Trading in the Buff. Zarghamee, H. (2014). Understanding Increases in Overnight Stock Returns.

Tags: forex signals, trading

Share on Facebook