Speculative Trading Research Articles

Unlocking the Reversal Anomaly in the A-share Market: Insights from Marginal Funds of Institutional Investors

Fama's Efficient Market Hypothesis (1970) posits that all valuable information is swiftly reflected in stock prices in mature markets. However, anomalies like momentum and reversal effects persist in China's A-share market. This study investigated the short-term reversal effect using turnover rates and institutional investor marginal funds. Significant reversals were found in high-turnover stocks due to speculative trading and deviations from intrinsic value. Institutional investors played a crucial role in correcting mispricing. The Institutional Holding Change Ratio (IHCR) demonstrated that institutional investors' marginal funds strongly explain the reversal anomaly and act as a style factor with positive returns. This research highlights the impact of institutional and individual investor behaviors on market efficiency and investment strategies.

Measuring speculation beyond day trading and bets on lottery-like stocks

We offer a new metric of stock market speculative intensity: the proportion of share purchases that are swiftly reversed, i.e., sold within a short time window. An example of this metric is a 14-day trading horizon. We use it to identify retail traders who aim for quick profits. Based on a vast set of trading accounts in Belgium (2003−2012), we observe that the scale of quick round-trip trading is far above what would be seen with a single focus on day trading. Simply put, amateur speculation is not limited to day traders. Measuring speculative trading through quick round-trip transactions delivers a variety of insights. Amateur speculators are more likely to be male, younger, with lower sophistication and lower levels of wealth. They hold concentrated portfolios and focus their efforts on only a few stocks at a time. They earn low returns, and they are prone to the disposition effect. They are liable to bet on lottery-like stocks, and their passion for trading is persistent.

Assessing the Pandemic Aviation Crisis: Speculative Behavior, Government Bail Outs, and Accommodative Monetary Policy

The COVID-19 pandemic was a health, economic, and financial crisis. The aviation sector was one of the most severely hit. Despite the extensive literature on this, COVID-Finance has been focused on stock returns, neglecting what could be learnt from the spreads of airlines’ credit default swaps (CDSs). This would seem of the utmost importance, given the epicenter of the crisis within the credit market. In this paper, an in-depth analysis of airlines’ CDS spreads is conducted. It is found that they were severely hit, for all airlines studied. However, the results of the PSY test showed that speculative trading led the surge, as explosive roots were found in the spreads of all these aviation firms. The dramatic increase in CDS spreads has contributed to already high borrowing costs for airlines. Our results suggest that aviation bail outs have helped to mitigate spreads’ explosiveness. Monetary policy measures have also limited, albeit indirectly, the funding risk posed by the government bail outs. By the end of March 2021, spreads were no longer explosive, and were approaching, at highly heterogeneous paces, their pre-pandemic values. Notwithstanding, airlines’ stock prices have been notably resistant to recovery.

Anti-Persistent Values of the Hurst Exponent Anticipate Mean Reversion in Pairs Trading: The Cryptocurrencies Market as a Case Study

Pairs trading is a short-term speculation trading strategy based on matching a long position with a short position in two assets in the hope that their prices will return to their historical equilibrium. In this paper, we focus on identifying opportunities where mean reversion will happen quickly, as the commission costs associated with keeping the positions open for an extended period of time can eliminate excess returns. To this end, we propose the use of the local Hurst exponent as a signal to open trades in the cryptocurrencies market. We conduct a natural experiment to show that the spread of pairs with anti-persistent values of Hurst revert to their mean significantly faster. Next, we verify that this effect is universal across pairs with different levels of co-movement. Finally, we back-test several pairs trading strategies that include H<0.5 as an indicator and check that all of them result in profits. Hence, we conclude that the Hurst exponent represents a meaningful indicator to detect pairs trading opportunities in the cryptocurrencies market.

Competition among high-frequency traders and market quality

I study how competition among high-frequency traders (HFTs) affects their trading behavior and market quality. The analysis exploits a unique dataset, which allows comparing environments with and without high-frequency competition, and contains an event - a tick size reform - which I use to separate the effects of high-frequency trading competition from the effects of the rising share of high-frequency trading in the market. I find that when HFTs compete in a stock, their speculative trading increases. Furthermore, market quality in that stock deteriorates. My findings hold for a variety of market quality and high-frequency trading behavior measures.

Informed traders, beauty contest and stock price volatility: Evidence from laboratory markets

AbstractWhether informed investors (e.g., professionals) trade stocks based on the fundamentals or ride on market sentiment is an empirical question, but few attempts have been made due to the lack of field data. We, therefore, conduct a laboratory experiment and examine if informed investors trade stocks based on their anticipation of less‐informed investors' valuation. In our experimental market, informed traders know the fundamental value of the stock; less‐informed traders do not know the accurate value and their valuation may be biased upwards or downwards. We find that informed traders ride on the (mis)valuations of less‐informed traders, and the speculative trades of the informed traders significantly affect stock prices. Our experimental result explains why stock prices are subject to the mass psychology sentiment even in markets with informed investors.

The impact of the capital gains tax on the Korean derivatives market

After lively debates on whether there was too much trading in the Korean derivatives market, the Korean government decided in 2016 to introduce a capital gains tax on KOSPI 200 futures and options. The stated goals were to secure tax revenue and restrict speculative trading. This article aims to assess the impact of this tax on the liquidity of the Korean derivatives market. We take advantage of an ad hoc delay between the taxation of KOSPI and Mini-KOSPI derivatives to apply a difference-indifferences analysis. The introduction of the capital gains tax reduced market activity for KOSPI 200 derivatives, but its extension to Mini-KOSPI 200 derivatives had no significant impact. Moreover, we do not find any significant effect on the bid–ask spread. A closer look at different types of traders also shows a decrease in the trading activity of individuals, compared to other traders who are exempt.

CRYPTOCURRENCY MARKET DYNAMICS: TRENDS, RISK AND REGULATORY CHALLENGES

The cryptocurrency market is a rapidly evolving landscape, characterized by its innovative nature and potential for significant economic disruption. This abstract delves into the intricate dynamics of the cryptocurrency market, focusing on the latest trends, inherent risks, and the burgeoning regulatory challenges that shape its future. Recent years have witnessed a surge in cryptocurrency adoption, with digital assets becoming increasingly mainstream. But there have been risks associated with this increase as well. One distinguishing characteristic of the market is still its volatility, which is fueled by mood in the market and speculative trading. Offers particular risks as well, such as worries about security and the possibility of illegal activity. Global regulatory organizations are struggling to provide comprehensive frameworks while encouraging creativity. The SEC’s recent actions against high-profile entities for offering unregistered securities highlight the intensified scrutiny within the sector. Moreover, policy adjustments by major exchanges, such as Hash Key Exchange’s halt on transactions with Binance, reflect the complex interplay between market operations and regulatory compliance. This abstract emphasizes the necessity of a well-rounded strategy that protects investors without impeding the technological breakthroughs that cryptocurrencies offer. As participants in navigating the cryptocurrency ecosystem effectively.

The iSPAC

Special purpose acquisition companies (SPACs) are one of the most celebrated investment vehicles in recent years. Relative to traditional IPOs, SPACs are much more cash-strapped and speculative. Resultantly, the scope for SPACs remains sparse for certain segments of the financial system notwithstanding the SPAC euphoria surrounding the financial markets: one notable exception is Islamic banking and finance. The Islamic banking business model is based upon the ethical ontologies and epistemologies – informed by the divine sources of Quran and Sunnah: the Shariah – operating with the mandate to promote socio-economic justice through a fair redistribution of wealth while embargoing speculative trading or investments and adopting a risk-sharing model between economic agents. Unsurprisingly, – owing to the speculative nature of SPACs – the Islamic finance industry remains reluctant to participate in the SPAC-mania despite the frenzy engulfing global securities markets. This work addresses the misaligned incentives inherent in a conventional SPAC structure and proposes alternative SPAC structure terms i.e., the iSPAC, which potentially mitigates the noted misaligned incentives and offers less dilutive SPAC terms to shareholders. Specifically, iSPAC structure terms address the issues of speculation (gharar), information asymmetry, and transparency in the pre-IPO phase, which may lead to adverse selection and moral hazard. Equally, the proposed structure reconciles post-IPO operational and investment-related risks such as the treatment of proceeds, interest rate (riba), opportunity costs, and management costs in consort with unethical behavior i.e. cashing-out opportunities that may lead to uneven redistribution of wealth thereby, widening the socio-economic voids in the society.

Fear of Missing out on Financial Gains: Associations Between Fear of Missing Out, Problem Gambling, and Speculative Trading in College Students

In recent years a fear of missing out (FOMO) on short-term monetary gains through speculative trading has been highlighted as a driving force of financial behaviors. Additionally, increasing evidence has likened speculative trading to gambling. The current study sought to determine whether financial FOMO is linked to stock market and cryptocurrency trading activities and problem gambling severity in both traditional gambling and financial trading domains, among a sample of 258 college students. Results of binomial regression and hurdle model analyses found that financial FOMO was linked to participation in stock market and cryptocurrency trading. Financial FOMO was also associated with problem gambling severity in traditional gambling and the presence of gambling problems in the stock market trading domain. Our results suggest that financial FOMO may be a salient risk factor of problem gambling in traditional gambling domains and, to a smaller degree, in the speculative trading domain among young adults.

Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price

European Union allowances (EUAs), the “currency in circulation” of the EU Emissions Trading Scheme (ETS), have spawned a great deal of speculative trading. This study proposes a model-driven long short-term memory network (LSTM)-convolutional neural network (CNN) hybrid model that integrates numerical data features and candlestick features to achieve accurate and unbiased structural prediction of EUA futures open-high-low-close (OHLC) prices during the four phases of the EU ETS. During EU ETS Phase IV, the out-of-sample prediction outcomes of the LSTM-CNN model exhibited a mean absolute percentage error (MAPE) of 0.942%, a mean absolute error (MAE) of 0.877, a root mean squared error (RMSE) of 1.157, a goodness-of-fit (R2) of 0.953, an accuracy ratio (AR) of 0.544, and a forecast correct rate of ups and downs (UP) of 0.579. In comparison to Naive methods, vector autoregression (VAR) combined with vector error correction model (VECM), multiple linear regression (MLR), partial least squares (PLS), support vector regression (SVR), and standalone LSTM, the LSTM-CNN approach demonstrated a notable enhancement in the average MAPE across the four stages of the EU ETS—specifically, an improvement of 21.66%, 43.15%, 15.73%, 15.72%, 10.45%, and 5.91%, respectively. Drawing from the unbiased structural forecasts of OHLC data, this study proposes fruitful intraday trading strategies that attain substantive investment returns in the realm of EUA futures trading. The multimodal forecasting methodology and intraday trading strategies advanced in this study hold considerable promise within the domain of energy finance.

Adverse selection and search congestion in over-the-counter markets

Asymmetric information about both private valuations of assets and their quality gives rise to uncertainty over sellers’ motives of trade, allowing high-valuation holders of low-quality assets to engage in speculative trades that involve no allocative gains. When sellers compete to find buyers, such speculative behaviour not only dilutes the average quality of assets but also creates a welfare-detrimental congestion externality that lengthens the time on market for each individual seller. A market designer can mitigate the inefficiencies by imposing a transaction tax and, in the case of severe adverse selection, limiting market participation.

Speculative Trading and Price Momentum

Speculative Trading and Price Momentum

Market Impact Analysis of Financial Literacy among A-Share Market Investors: An Agent-Based Model.

Financial literacy has become increasingly crucial in today's complex financial markets. This paper explores the impact of financial literacy on the stock market by establishing an artificial financial market that aligns with the characteristics of the Chinese A-share market using agent-based modeling. The study incorporates financial literacy into investors' mixed beliefs and simulates their behavior in the market. The results show that improving individual investors' financial literacy can improve market quality and investor performance, as well as reduce the unequal distribution of wealth to some extent. However, the phenomenon of speculative trading and irrational behavior in the market can pose potential risks that require regulatory measures. Thus, policy recommendations to improve individual investors' financial literacy and establish corresponding regulatory measures are proposed.

Risk-return tradeoff and serial correlation in the Chinese stock market: A bailout-driven crash feedback hypothesis

This paper explores two puzzling phenomena about aggregate stock market behavior: the weak mean–variance (risk-return) relation and return autocorrelation. Existing literature typically analyzes these two phenomena in isolation, with few attempts to understand their coexistence. To bridge this gap, we integrate two key factors, market-wide bailout guarantees and feedback trading, within the context of the Chinese stock market. We develop joint pricing formulas to theoretically show that feedback traders’ speculation on bailouts can simultaneously produce these anomalies by tying them to features typical of market crashes. Our empirical analysis, based on Chinese market data from 1997 to 2020 and a market crash index, provides consistent support for these theoretical derivations. The results highlight the significant role of speculation driven by bailout expectations in influencing aggregate stock market dynamics. Accordingly, this also illuminates the necessity for further research on the extensive pricing implications of both government intervention and speculative trading.

‘LAND as GOLD’: Commodification of Agricultural Land and its Implications in Andhra Pradesh

Agricultural-land sale prices in India exponentially increased during post-reforms period. This article studies operation of agricultural-land sale market and its implications in Andhra Pradesh during 2001–2017. It is based on primary data from six villages in three regions. The results show that sale prices of unit agricultural land have skyrocketed, and they are no way related to returns from land. The rich, non-cultivating households are increasingly buying agricultural land at higher prices for non-cultivating purposes and left fallow. They treat agricultural land as gold for store value, speculative trading, tax benefits, etc. The higher land sale prices along with depleting returns tempted distressed small farmers to sell their land. Consequently, cultivating small farmers are pushed to supply-side of land market as distressed sellers but not on the demand side of land market as buyers. Agricultural land is slowly but permanently going away from the hands of cultivator to noncultivators. Continuation of these trends has an adverse impact on equity and efficiency in agriculture land ownership in the state.

A comparison of machine learning and econometric models for pricing perpetual Bitcoin futures and their application to algorithmic trading

AbstractBitcoin (BTC) perpetual futures contracts are highly leveraged speculative trading instruments with daily market trading of $45 Billion. BTC perpetual futures are derivative contracts, which depend upon the underlying BTC SPOT (current) price. Pricing perpetual futures fairly is hard, using traditional arbitrage arguments, because of the volatile nature of the so called funding rate, which is used as the replacement of risk free rate in the Cryptocurrency market. This work presents a novel technique for pricing BTC futures contracts using conditional volatility and mean models. Intra‐day high‐frequency futures' return volatility and mean are modelled using different ML and econometric techniques. A comparison is made using statistical measures to find the model that best captures the intra‐day conditional mean and volatility. Exponential generalized autoregressive conditional heteroskedasticity is shown to be an almost unbiased predictor of intra‐day volatility, while a constant autoregressive moving average (0, 0) model best captures the conditional mean of the returns. A market directional high frequency trading algorithm is developed using the volatility and mean models. The algorithm first prices the futures contract at some future point of time using the volatility and mean regression models. Next, the slope between the current futures price and the expected price are used to predict the market direction. A long or short position is taken depending upon the expected market direction movement. Extensive back‐testing results show absolute returns of 1500%–8000% depending upon the transaction fees and leverage used. On average, the market direction is predicted correctly 85% of the time by the best model. Finally, the trading technique is market neutral, in that it gives large positive returns, with low SD, in both bull and bear markets.

Behavioural Biases in Equity, Futures, and Options Trading: Implications for Market Efficiency

This research paper presents a comprehensive analysis of behavioural biases among traders operating in the equity, futures, and options segments of financial markets. The study aims to understand the psychological factors influencing trading decisions, risk perception, and trading strategies. By exploring the impact of behavioural biases on market efficiency and stability, this research contributes to the growing field of behavioural finance. Using a mixed-method approach, the study collected data through surveys, interviews, and behavioural experiments from a diverse group of traders, including retail investors and institutional participants. The analysis focused on identifying common behavioural biases such as overconfidence, loss aversion, herding behaviour, anchoring, and the disposition effect. The findings reveal that behavioural biases are prevalent among traders in all three segments. Overconfidence leads to excessive trading and underestimation of risks, while loss aversion influences risk aversion and reluctance to exit losing positions. Herding behaviour results in the formation of market bubbles and crashes, and anchoring affects traders' reliance on past information to make decisions. The disposition effect influences traders to hold onto losing positions longer than profitable ones. The study also highlights the impact of behavioural biases on trading strategies and risk management practices. Traders tend to exhibit risk-seeking behaviour during periods of market euphoria and become risk-averse during times of uncertainty and volatility. Moreover, behavioural biases can lead to suboptimal portfolio allocation and performance outcomes. The implications of these findings for market efficiency and stability are significant. Behavioural biases can contribute to market inefficiencies, distorted price patterns, and increased market volatility. As a result, market participants and regulators must address these biases to foster more informed decision-making and improve market outcomes. To mitigate the impact of behavioural biases, the study recommends trader education programs focusing on behavioural finance principles and risk management techniques. Additionally, market regulators should implement measures to promote transparency and discourage excessive speculative trading. In conclusion, this research underscores the importance of understanding behavioural biases in equity, futures, and options trading. By shedding light on the psychological drivers of trading behaviour, this study provides valuable insights for traders, financial institutions, and policymakers to enhance market efficiency and stability in the face of behavioural challenges.

"Buy High, Sell Low": A Qualitative Study of Cryptocurrency Traders Who Experience Harm.

The constant, substantial price fluctuations of cryptocurrency allow traders to engage in highly speculative trading that closely resembles gambling. With significant financial loss associated with adverse mental health outcomes, it is important to investigate the impact that market participation has on mental health. Therefore, we conducted interviews with 17 participants who self-reported problems due to trading. Thematic analysis was conducted revealing themes: (1) factors in engagement, (2) impacts of trading and (3) harm reduction. Factors in engagement captured factors that motivated and sustained cryptocurrency trading. Impacts of trading outlined how cryptocurrency trading positively and negatively impacted participants. Harm reduction described methods participants employed to reduce mental distress from trading. Our study provides novel insights into the adverse impacts of cryptocurrency trading across multiple domains, especially mental health, relationships and finances. They also indicate the importance of further research on effective coping strategies for distress caused by financial loss from trading. Additionally, our study reveals the significant role social environments play on participants' expectations and intentions regarding cryptocurrency trading. These social networks extend beyond real-life relationship to include celebrity and influencer endorsement. This encourages investigation into the content of cryptocurrency promotions and the influence they have on individuals' decision to trade.

An Empirical Study on the Stock Market Reaction to Corporate Green Bond Issuance in China

China has become one of the major green bond issue countries and is the only developing country. Research on green corporate bonds is currently a novel direction, and very few studies focus on the Chinese market. Researching Chinese corporate green bonds is essential for the Chinese and merging market. This article discusses corporate green bonds in the Chinese market from the perspective of stock market reaction, institutional investor positions and cost of capital. Firstly, the author analyzes the Chinese stock market reaction to the issuance of corporate green bonds by using an event-study methodology. The paper finds that Chinese stock markets respond positively to green bond issuance announcements, but this response reflects speculative trading behavior. Secondly, the author compares the stock positions of institutional investors after the issuance of corporate green bonds and finds that green corporate bond issuance in the Chinese market can attract long-term investors (institutional investors). Finally, this author adopts the exact matching method to verify the cost of capital argument and finds no significant benefit to the cost of capital, which is consistent with prior works.