Profitable Trading Research Articles

Market efficiency of cryptocurrency: evidence from the Bitcoin market

This study examines whether the Bitcoin market satisfies the (weak-form) efficient market hypothesis using a quantum harmonic oscillator, which provides the state-specific probability density functions that capture the superimposed Gaussian and non-Gaussian states of the log return distribution. Contrasting the mixed evidence from a variance ratio test, the high probability allocated to the ground state suggests a near-efficient Bitcoin market. Findings imply that as Bitcoin evolves into an efficient market, speculators might encounter difficulty in exploiting profitable trading strategies. Furthermore, when policymakers initiate tight regulations to control the market, they should closely monitor market efficiency as an index of price distortion.

Information linkages in a financial market with imperfect competition

This study proposes a tractable imperfectly competitive economy where traders are socially connected with each other via an information network. We investigate the impact of information linkages on market equilibrium outcomes. In the linear-quadratic-normal framework, we first establish the existence and uniqueness of symmetric linear Bayesian Nash equilibrium. We then show that an increase of the level of information linkages decreases price impact, belief disagreement, return volatility and trading volume. Additionally, unlike the non-monotonicity results in large economies, we also show that increasing the level of information linkages always improves liquidity, increases price volatility, and decreases trading profits in our imperfectly competitive economy.

Meta-Labeling: Calibration and Position Sizing

Meta-labeling is a recently developed tool for determining the position size of a trade. It involves applying a secondary model to produce an output that can be interpreted as the estimated probability of a profitable trade, which can then be used to size positions. Before sizing the position, probability calibration can be applied to bring the model’s estimates closer to true posterior probabilities. This article investigates the use of these estimated probabilities, both uncalibrated and calibrated, in six position sizing algorithms. The algorithms used in this article include established methods used in practice and variations thereon, as well as a novel method called sigmoid optimal position sizing. The position sizing methods are evaluated and compared using strategy metrics such as the Sharpe ratio and maximum drawdown. The results indicate that the performance of fixed position sizing methods is significantly improved by calibration, whereas methods that estimate their functions from the training data do not gain any significant advantage from probability calibration.

Enforcement of Non‐Compete Agreements, Outside Employment Opportunities, and Insider Trading*

ABSTRACTEnforcement of non‐compete agreements could affect executives' and directors' incentives to profit from their information advantage. This is because excessive trading profits could result in job termination, which would trigger the restrictions imposed by the non‐compete agreements. We find that executives' and directors' insider trading profits from sales are lower for companies headquartered in states with greater enforcement of non‐compete agreements. The path analyses suggest that high enforcement of non‐compete agreements disincentivizes managers to profit from their information advantage to avoid the possibility of job termination and the cost of job terminations. We also find that insiders in companies headquartered in states with greater enforcement of non‐compete agreements are less likely to exploit their information advantage by timing their sales before unfavorable corporate earnings announcements. The results suggest that enforcement of non‐compete agreements reduces executives' and directors' incentives by imposing costs on future outside employment opportunities.

Application of Market Cycle Analysis and LSTM in Prediction of Stock Price Movements

The stock market prediction has been carried out by several ways in data science using deep learning approaches to capture profitable trading opportunities and making the trading plans. However, it is widely believed there are two main issues involved in it, i.e., efficient market hypothesis and low information noise ratio. Therefore, a prediction based model will be affected by noises thus hard to produce a prediction. In this paper, two methods will be presented for forecasting stock future performance. To be specific, LSTM (long-short time memory) and cycle analysis are implemented to predict the future period that gives a higher return than average times. According to the analysis, introducing the time analysis as a variable to input could significantly increase the accuracy of predicting the return for the next few weeks. These results shed light on guiding further exploration of the different ways of extracting periodic behaviors of the market and marking predictions based on the analysis.

Comparative Study of Pair Trading Techniques in Pakistan’s Financial and Non-Financial Sector

Purpose: This study attempts to empirically investigate the pair trading performance of financial and non-financial firms in Pakistan. Methodology: Daily data from 2008 to 2017 was collected for nine years. Cointegration and the distance approach were the two major analytical techniques used to evaluate the profitability of pair trading. The financial and non-financial sectors of the Pakistan Stock Exchange were used to build the pairs. Findings: Results showed that the top 5 pairs of portfolios exhibited the highest average excess returns of 0.0698, and Jensen's alpha is 0.0947 for the top 5 pairs. All pairs of firms showed significant and positive risk-adjusted performance. In the non-financial sector, the Top 10 pairs of portfolios had the highest average excess returns of 0.0789, and Jensen's alpha under the co-integration method for non-financial firms for all pairs 5, 10, 15, and 20 of the portfolios is also substantial and positive for risk-adjusted performance, with 0.0046, 0.0618, 0.0577, and 0.0493, respectively. Finally, pair trading under both techniques showed profitability. However, the co-integration technique exhibited better performance than the distance method. Conclusion: The study concluded that both pair trading techniques, particularly the co-integration technique, exhibited profitable pair trading performance that can assist investors and fund managers to earn positive returns on their investments regardless of market direction.

Does common ownership constrain managerial rent extraction? Evidence from insider trading profitability

This study identifies a new economic benefit of common institutional ownership, which refers to the increasingly contentious phenomenon of U.S. firms sharing stockholders with their industry competitors. We find a significantly negative relation between common ownership and insider trading profitability. The disciplinary effect of common ownership on opportunistic insider trading is particularly evident when the information effects of common ownership are greater, when common owners are more likely to benefit from positive governance externalities, and in the subset of trades made by opportunistic insiders. Using the exogenous variations in common ownership induced by financial institution mergers, we conduct a difference-in-differences analysis and find consistent results. We also provide evidence that common owners encourage firms to impose ex-ante restrictions on insider trading and take ex-post actions to discipline opportunistic insiders by voting against management. Overall, our findings suggest that common institutional shareholders have information advantages, governance incentives, and effective means to constrain opportunistic insider trading.

The Information in Industry-Neutral Self-Financed Trades

AbstractWe identify Industry-Neutral Self-Financed Informed Trading (INSFIT) as stock trades financed by offsetting, equivalent dollar-denominated stock trades in the same industry. Approximately 37% of short-term mutual fund trading profits can be attributed to these trade pairs. Consistent with informed trading, INSFIT precedes unusually high media coverage for the underlying stocks. The trades underlying INSFIT are also larger as the release of stock-level news becomes more imminent. Both relative valuation and the hedging of industry exposure motivate INSFIT’s industry neutrality. While INSFIT positively impacts fund performance, active fund managers who execute INSFIT more aggressively obtain smaller trading profits per execution.

Insider trading in multinational firms

AbstractWe explore insider trading at multinational firms and find multinational firm insiders make larger trades followed by larger abnormal returns relative to those at domestic firms. Multinational firm insiders profitably purchase underpriced, value stocks with low past returns. They trade more profitably, not only because of market pricing opportunities, but also because of the advantageous timing of informed trades before earnings announcements. Insider trading profits are highest at multinational firms with foreign sales in regions culturally and linguistically distinct from the United States. These findings are consistent with opportunistic insiders strategically profiting from difficult to process foreign information.

Influence of U.S. Institutions, Policies and Agreements on Latin American Economy from 2000-2020

Latin American countries have taken advantages of economic development in decades, slowly becoming most important trading partners for major economic entities. In many observations, it seems that one of the most significant diving forces is the United States, which is in the adjacent geographic location. This paper focuses on the topic related to the extent to which the US foreign policies, as well as the US-lead international organizations, and the economic development of Latin American Countries correlate. Analysis of data on U.S. trade with Latin America and official policy documents is from four perspectives: The influence of transnational corporations, the influence of trade organizations, the negative influence of US sanctions, and the positive impact of US trade policies, in order to provide a clear answer for the topic about the interdependence of Latin America's economic development, bringing a more comprehensive explanation to the U.S.-Latin America trade agreements. It is observed and concluded that, contradicted to what has often been assumed, U.S. multinational corporations have not had much impact on the local market, yet Latin American trade with the U.S. is undoubtedly driving the Latin American economy. Trade organizations, such as the WTO, play positive roles in the development of the Latin American economy, and the Latin American countries are also contributing to the reform processes of trade organizations to maximize the benefits. Lastly, the sanctions from the US is constantly holding negative impacts to some Latin American countries, whereas profitable trade policies bring advantages for other Latin American countries.

The EU Carbon Border Adjustment Mechanism will exacerbate the economic-carbon inequality in the plastic trade

The EU Carbon Border Adjustment Mechanism (CBAM) is regarded as the world's first carbon tariff regulation. Its consequential impacts on the trades of individual countries/regions at different development stages are crucial for climate justice while remaining unclear. By combining Multi-regional Input-output analysis and scenario analysis, this study analyzed the economic-carbon inequality in national/regional plastic exports and simulated CBAM's short-term impacts on plastic trade inequality under different scenarios. Our analysis shows that the maximum values of total carbon tariffs for all countries/regions levied on plastic exports to the EU will be 497.8, 859.1, and 1564.2 million euros under scenarios covering Scope 1, Scope 1&2, and Scope 1&2&3 emissions, respectively. The corresponding proportions of CBAM costs to national/regional plastic export volumes to the EU will be 0.6%, 1.0%, and 1.8% on average, respectively. China and the rest of Asia and the Pacific will burden the most CBAM costs in all cases, and Russia will be the country most affected. CBAM will exacerbate the economic-carbon inequality in the plastic trade by reducing the trade profits of developing economies with higher ratios than those of developed economies. Our analysis calls for practical initiatives to induce technological advances toward lower carbon technologies, increase trade diversification, exploit the domestic market's potential, and implement domestic carbon pricing mechanisms to alleviate the negative impact of CBAM.

The Impact of the Closing of the Tanjung Batu Merah Localization Place on Community Income in RT 002/ RW 010 Sirimau District, Ambon City

This study aims to analyze the impact of the closure of the Tanjung Batu Merah localization site, Ambon City, on the income of community retail traders around the Tanjung Batu Merah Localization. The sample in this study were 32 retail traders. The data collection technique in this study was a questionnaire. The analysis technique used in this study was t-test analysis. The t-test used was a pair sample t-test. Normality, Homogeneity test, Linearity test. The results showed that there was a significant difference in the profit of retail traders, before and after the closing of the localization place, the decrease in the average profit of retail traders before and after the closing of the localization place was seen from the average profit of the traders before the closing of the localization place of Rp. 1,326. 562, - decreased to Rp. 568,750, - after the localization place was closed, as well as the income of retail traders, seen from the average income of traders before the localization place was closed, amounting to Rp. 2,627,812, - decreased to Rp. 1,102,625, - after the localization place was closed. Meanwhile, the sign value of the t-test obtained was 0.000 0.05 indicating a significant impact on the income and profits of retail traders in the Tanjung community, Batu Merah Village.

A Rational Risk Policy? Why Path Dependence Matters

The Kelly criterion determines optimal bet sizes that maximize long-term growth. While growth is definitely an important consideration, the focus on growth alone can lead to significant drawdowns, leading to psychological discomfort for a risk-taker. Path-dependent risk measures, such as drawdown risk, provide a means to assess the risk of significant portfolio retracements. In this paper, we provide a flexible framework for assessing path dependent risk for a trading or investment operation. Given a certain set of profitable trading characteristics, a risk-taker who maximizes expected growth can still be faced with significant drawdowns to the point where a strategy becomes unsustainable. We demonstrate, through a series of experiments, the importance of path dependent risks in the case of outcomes subject to various return distributions. Based on Monte Carlo simulation, we analyze the medium-term behavior of different cumulative return paths and study the impact of different return outcome distributions. We show that in the case of heavier tailed outcomes, extra care is needed, and optimal might not be so optimal in the end.

Religion and insider trading profits

We use the controversial aspect of insider trading to analyze the impact of local social norms on insiders’ profits. We argue that religiosity is a source of social norms curbing self-interested behavior and, accordingly, it limits corporate insiders’ opportunistic trading on private information. Our results confirm that trades by insiders in firms located in more religious areas are followed by lower profits, those insiders are less likely to trade on future earnings news, and their trades are less likely to be opportunistic. The effect of religion on profitability of insider trading holds across different levels of disclosure environments and is more pronounced in firms with poor corporate governance. Overall, we offer new insights into the effect of social norms on individuals’ financial decisions.

A multi-agent virtual market model for generalization in reinforcement learning based trading strategies

Many studies have successfully used reinforcement learning (RL) to train an intelligent agent that learns profitable trading strategies from financial market data. Most of RL trading studies have simplified the effect of the actions of the trading agent on the market state. The trading agent is trained to maximize long-term profit by optimizing fixed historical data. However, such approach frequently results in the trading performance during out-of-sample validation being considerably different from that during training. In this paper, we propose a multi-agent virtual market model (MVMM) comprised of multiple generative adversarial networks (GANs) which cooperate with each other to reproduce market price changes. In addition, the action of the trading agent can be superimposed on the current state as the input of the MVMM to generate an action-dependent next state. In this research, real historical data were replaced with the simulated market data generated by the MVMM. The experimental results indicated that the trading strategy of the trained RL agent achieved a 12% higher profit and exhibited low risk of loss in the 2019 China Shanghai Shenzhen 300 stock index futures backtest.

Internal Relevance between Analysts’ Forecasts and Target Prices - Informativeness and Investment Value

ABSTRACT Analysts’ decision-making process, through which they use earnings and non-earnings forecasts to provide their target price revisions, remains opaque. Based on the decision tree analysis, we develop a new multivariate information metric, Internal Relevance (IR), to measure the extent to which the revisions of analysts’ consensus earnings and sales forecasts are incorporated into the revisions of consensus target prices. We show that stocks with higher IR have stronger market reactions to target price revisions in terms of abnormal return, abnormal trading volume, and abnormal return volatility. These results remain consistent across a series of robustness checks. Finally, we perform portfolio analysis to show that IR can be used to screen stocks to improve trading profitability.

Shortlisting machine learning-based stock trading recommendations using candlestick pattern recognition

Quantitative stock trading systems apply automated, data-driven models to invest in the stock markets. In the last two decades, the machine learning community has deeply explored the use of per-stock machine learning models to forecast next-day stocks’ directions and generate profitable trading signals. On the basis of the experience of discretionary traders, some promising attempts to enhance classifier performance by integrating the knowledge extracted from Japanese candlestick charts have been made. However, machine learning-based trading systems tend to generate an excessive number of false signals and do not necessarily consider the information provided by candlestick patterns in the appropriate manner. To alleviate these negative effects, this paper proposes to decouple the machine learning and pattern recognition steps so that the trading system can generate a reduced number of double-checked trading recommendations. Specifically, it proposes to selectively filter out the machine learning-based trading recommendations that are deemed as potentially unreliable according to the recognized graphical patterns. To this aim, it explores various alternatives to combine pattern recognition strategies with different machine learning models, including various shallow and deep supervised models and autoregressive techniques. The experiments, carried out on different market exchanges and under different conditions, demonstrate the effectiveness of the proposed approach in terms of return of investment and maximum drawdown of the trading system.

Are Large Traders Harmed by Front-running HFTs?

This paper studies the influences of a high-frequency trader (HFT) on a large trader whose future trading is predicted by the former. We conclude that HFT always front-runs and the large trader is benefited when: (1) there is sufficient high-speed noise trading; (2) HFT's prediction is vague enough. Besides, we find surprisingly that (1) making HFT's prediction less accurate might decrease large trader's profit; (2) when there is little high-speed noise trading, although HFT nearly does nothing, the large trader is still hurt.

ШЛЯХИ УДОСКОНАЛЕННЯ ОРГАНІЗАЦІЙНИХ СТРУКТУР ТОРГОВЕЛЬНИХ БІЗНЕС-СТРУКТУР: ТЕОРЕТИЧНИЙ АСПЕКТ

Goal. Determination of the optimal organizational structure of the trade business structure and identification of promising directions for its development. Method. The theoretical and methodological basis of the research is the fundamental and modern work of foreign and domestic specialists. The analysis of existing types of organizational structures of trade business structures is based on the methods of analysis and synthesis, induction and deduction. The study of prospects for the development of the organizational structure of trade business structures is based on the methodology of scientific abstraction. Results. Provision of effective organization of the trade business structure was considered, which will help increase the quality of trade service, speed up the circulation of goods, increase the profitability of trade enterprises as a whole, and in the future will contribute to the development of the country's economy. The current state of the trade industry was analyzed, and it was determined that the current state of the trade industry has significant potential for development, which contributes to increasing the competitiveness of the trade sector of Ukraine and creates new opportunities for business. Modern types of organizational structures of the trade business structure were analyzed, the main advantages and disadvantages of each type of organizational structure were identified. It is substantiated that the functioning of a powerful trading business structure should be built on the basis of a matrix management system, which will allow combining flexibility and coordination between different departments and ensure interaction between functional and project groups, which contributes to improving management efficiency. It was determined that the ways to improve the organizational structure of the trade business structure should include the following elements: motivation, organizational and legal element, methodical element, informational element, social and psychological, personnel, material and technical and element of financial support Scientific novelty. The current state of the trade industry and its development prospects were analyzed. The main organizational structures of trade business structures existing at the current stage of development were analyzed. Practical significance. The main directions for improving the organizational structure of the trade business structure based on the matrix structure were formulated. The main conclusions were made and recommendations were given regarding further ways of improving the matrix organizational structure for trade business structures.

THE WAYS OF IMPROVING ORGANIZATIONAL STRUCTURES OF COMMERCIAL BUSINESS STRUCTURES: THEORETICAL ASPECT

Goal. Determination of the optimal organizational structure of the trade business structure and identification of promising directions for its development. Method. The theoretical and methodological basis of the research is the fundamental and modern work of foreign and domestic specialists. The analysis of existing types of organizational structures of trade business structures is based on the methods of analysis and synthesis, induction and deduction. The study of prospects for the development of the organizational structure of trade business structures is based on the methodology of scientific abstraction. Results. Provision of effective organization of the trade business structure was considered, which will help increase the quality of trade service, speed up the circulation of goods, increase the profitability of trade enterprises as a whole, and in the future will contribute to the development of the country's economy. The current state of the trade industry was analyzed, and it was determined that the current state of the trade industry has significant potential for development, which contributes to increasing the competitiveness of the trade sector of Ukraine and creates new opportunities for business. Modern types of organizational structures of the trade business structure were analyzed, the main advantages and disadvantages of each type of organizational structure were identified. It is substantiated that the functioning of a powerful trading business structure should be built on the basis of a matrix management system, which will allow combining flexibility and coordination between different departments and ensure interaction between functional and project groups, which contributes to improving management efficiency. It was determined that the ways to improve the organizational structure of the trade business structure should include the following elements: motivation, organizational and legal element, methodical element, informational element, social and psychological, personnel, material and technical and element of financial support Scientific novelty. The current state of the trade industry and its development prospects were analyzed. The main organizational structures of trade business structures existing at the current stage of development were analyzed. Practical significance. The main directions for improving the organizational structure of the trade business structure based on the matrix structure were formulated. The main conclusions were made and recommendations were given regarding further ways of improving the matrix organizational structure for trade business structures.