Stop-loss Strategy Research Articles

Research on diversified fund allocation strategy

Money management is crucial in investing and is an important risk management tool. It can affect the magnitude of an investor's loss from a number of perspectives, including the reasonableness of position management, the effectiveness of capital allocation, the setting of stop-loss strategies, the degree of diversification of investments, and the regular evaluation and adjustment of capital flows. The purpose of this paper is to investigate the capital allocation issue in money management. Through a decade-long history of back-testing and evaluation of multiple indicators, this paper analyzes and compares three different methods of capital allocation. The methods mainly include the equal allocation of funds method, the profitability preference method, and the risk adjustment method. The results show that the Profit Preferred Method performs best in terms of strategy return and risk-adjusted return, while the Risk-Adjusted Method excels in the control of maximum retracement and is suitable for risk-averse investors. Despite the lower returns of the equal distribution method, its stability is still attractive.

Analysis of the Impact of Investor Sentiment on Stock Price Crash Risk

The stability of the stock market is very important to the country. Some researchers have found that sentiment has an impact on stock price crash risk. However, research mainly focuses on media sentiment and company sentiment, and research on investor sentiment is not mature enough. This article collates the literature to explore the relationship between investor sentiment and stock price crash risk. This article provides a systematic classification of indicators that measure stock price crash risk. Then, when introducing the methods of measuring investor sentiment, the indicators for measuring investor sentiment are classified, and the scope of application of the three mainstream methods is summarized. This article introduces three models commonly used in the academic community for this problem and conducts a comparative analysis of the three mainstream models. Research has found a positive relationship between investor sentiment and stock price crash risk. As crisis sentiment rises, a large number of investors will begin to implement stop-loss strategies and even sell at extremely low prices to minimize losses. As a result, the likelihood of a stock market price crash increases significantly. In addition, the selection of data sets affects the measurement of investor sentiment to a great extent, and the development and utilization of alternative data is a future research direction. Beyond this, there is reverse causation between investor sentiment and stock price crash risk. Therefore, it is necessary to consider reducing or avoiding the impact of endogeneity problems when selecting a model.

The out-of-sample performance of carry trades

We carry out a large-scale investigation of the reliability of the profitability of carry trade strategies, using foreign exchange data for 48 countries over 36 years, employing reality check, superior predictive ability test, and stepwise tests to correct for data-snooping bias (the factor of luck in model selection). Carry trade strategies chosen as profitable in one period are generally not profitable in an ensuing out-of-sample sample period, especially after correcting for data-snooping and even after allowing for learning and stop-loss strategies. Any evidence of consistency in carry trade profitability that is found is concentrated in a relatively brief historical period, 1998-2005. We further investigate particular currency pairs that may drive the out-of-sample profitability during this period, and find their performance to be unstable in general. Our findings thus highlight the instability and the importance of chance or luck in generating profits from carry trades.

Optimal stop-loss rules in markets with long-range dependence

Stop-loss is a common risk management tool for limiting risks and improving trading strategy performance. The effectiveness of stop-loss depends critically on asset price characteristics. This study is the first to analyze stop-loss strategy incorporating long-range dependence of asset prices through a fractional Brownian motion-based market model. It is shown that stop-loss strategy yields a positive return premium over the buy-and-hold return when asset price exhibits long-range dependence. The efficacy of stop-loss strategies and the determining criterions are investigated through both theoretical analysis and simulation studies. The performance of a stop-loss rule depends on the Hurst parameter, mean and volatility of the asset returns. The optimal stop-loss threshold model in a chosen strategy class is fitted by polynomial regression. Empirical analysis demonstrates that the class-specific optimal rules outperform stop-loss rules under alternative asset return-generating models.

Exploring the Effectiveness of Trailing Stop-Loss Strategies for Individual Investors

The authors empirically examine the effectiveness of trailing stop-loss (TSL) strategies in generating excess returns for individual investors. Using data from January 1, 2001, to December 31, 2021, the authors analyze four popular US-based market-level exchange-traded funds (ETFs) and nine sector-level ETFs. Using various fixed percentages and historical volatility levels to determine the TSL threshold rule (i.e., the downside stop price at which investors would exit a position), the authors find that low thresholds (i.e., narrower downside stop prices) yield significantly lower excess returns, and higher thresholds, typically between 1.0 and 1.5 standard deviations, provide significantly higher excess returns. Moreover, the vast majority of the TSL trading strategies post positive excess returns even after including transaction costs and systematic risks, regardless of the threshold level.

Optimality of Buy-and-Hold Strategies

The buy-and-hold way of investing has been taken as gospel by many professional investors since the 1960s. In recent years, however, it has come under harsh attack from both academics and practitioners who claim its ineffectiveness in the face of increasingly volatile markets. This research takes a theoretical approach to evaluating its effectiveness by invoking a powerful optimality theorem to gauge its effectiveness or, more specifically, its optimality level. In terms of optimality level, we determine how well it fares against three other popular strategies – lock-in, random-timing, and stop-loss. To make the concept of optimality level practically operational, we set up a two-factor model to depict the market environment and use Monte Carlo simulation to determine the optimality levels of these strategies. In terms of average optimality level, our results show that, in general, buy-and-hold strategies outperform the other three strategies in stable market environment, but they are outperformed by lock-in and stop-loss strategies in volatile market environment.

МОНИТОРИНГ И ОЦЕНКА РЕЗУЛЬТАТОВ УПРАВЛЕНИЯ ОПЕРАЦИОННЫМИ РИСКАМИ

The article discusses issues of operational risk management, allowing to minimize possible losses and improve resistance to crisis situations. Effective risk management ensures more stable operations and contributes to achieving the strategic goals of the enterprise. Monitoring and evaluating the results of operational risk management through portfolio diversification allows you to make informed strategic decisions. The author presents a methodology for managing operational risks that allows minimizing potential losses in the company. Practical examples illustrate the use of stop loss and correlation strategies to evaluate the results of crisis management. Monitoring and evaluation of operational risk management results is presented in the study as a process of systematic assessment and comparison of actual financial results with pre-planned values. The author substantiates that the development of strategies plays a critical role in financial management, ensuring the accuracy and efficiency of the operating process, allowing timely corrective measures to be taken and control over potential losses.

Prediction on the Value Trends of Bitcoin and Gold-on Account of ARMA Time Series Forecasting Model

In this paper, we aimed to build a quantitative investment trading model based on a combination of a multivariate cycle ARMA model and Apriori. We first note that in order to have a sound investment strategy, a forecast for the next trading day needs to be made. To do this, a basic time series forecasting model was first built to predict the value of gold and bitcoin for the next day based on the market volatility of the previous 40 days. The next step is developing a trading strategy model with a stable rate of return and some risk tolerance. At the same time, we developed a fixed stop-loss strategy to protect the strategy's stability and improve the risk resistance performance. Ultimately, using this model, we calculated that on 10 September 2021, we will have a return of $4816941 in Bitcoin and $1129.0503 in gold.

Deep Learning Models for Predicting Monthly TAIEX to Support Making Decisions in Index Futures Trading

Futures markets offer investors many attractive advantages, including high leverage, high liquidity, fair, and fast returns. Highly leveraged positions and big contract sizes, on the other hand, expose investors to the risk of massive losses from even minor market changes. Among the numerous stock market forecasting tools, deep learning has recently emerged as a favorite tool in the research community. This study presents an approach for applying deep learning models to predict the monthly average of the Taiwan Capitalization Weighted Stock Index (TAIEX) to support decision-making in trading Mini-TAIEX futures (MTX). We inspected many global financial and economic factors to find the most valuable predictor variables for the TAIEX, and we examined three different deep learning architectures for building prediction models. A simulation on trading MTX was then performed with a simple trading strategy and two different stop-loss strategies to show the effectiveness of the models. We found that the Temporal Convolutional Network (TCN) performed better than other models, including the two baselines, i.e., linear regression and extreme gradient boosting. Moreover, stop-loss strategies are necessary, and a simple one could be sufficient to reduce a severe loss effectively.

Realization utility with stop-loss strategy

We present a realization utility model with a stop-loss strategy, in which the investor will sell his position and leave the market instantly when the asset price drops to a certain preset level. The model gives the analytical solutions to the value function and the optimal liquidation point. We show that the stop-loss strategy will increase the value function and specifically the growth will be larger when the asset price is close to the stop-loss level, and smaller when the price is relatively high above from it. In addition, we show that the stop-loss strategy will decease the optimal liquidation point, which means it will induce the investor to voluntarily liquidate earlier at a lower take-profit level. Our results highlight the contribution of the stop-loss strategy to utility maximum, which could shed light on the value of this kind of strategies.

Efficiency of Dynamic Portfolio Choices: An Experiment

Abstract We study the efficiency of dynamic portfolio choices using the nonparametric methods of Dybvig (1988) and Post (2003). We compare a dynamic portfolio task against an equivalent static Arrow-Debreu problem under two alternative environments: (1) nonpooled with $2^T$ terminal states and (2) pooled with $T+1$ unique terminal states. The results suggest that, within each environment, efficiency is lower in a static format and when the number of final states is larger. In the nonpooled dynamic task, which allows for path dependent strategies, we find that a form of stop-loss strategy drives efficiency losses.

Automated trading systems’ evaluation using d-Backtest PS method and WM ranking in financial markets

Given the popularity and propagation of automated trading systems in financial markets among institutional and individual traders in recent decades, this work attempts to compare and evaluate such ten systems based on different popular technical indicators in combination – for the first time – with the d-Backtest PS method for parameter selection. The systems use the technical indicators of Moving Averages (MA), Average Directional Index (ADX), Ichimoku Kinko Hyo, Moving Average Convergence/Divergence (MACD), Parabolic Stop and Reverse (SAR), Pivot, Turtle and Bollinger Bands (BB), and are enhanced by Stop Loss Strategies based on the Average True Range (ATR) indicator. Improvements in the speed of the back-testing computations used by the d-Backtest PS method over weekly intervals allowed examining all systems on a 3.5 years trading period for 7 assets in financial markets, namely EUR/USD, GBP/USD, USD/JPY, USD/CHF, XAU/USD, WTI, and BTC/USD. To evaluate the systems more holistically, a weighted metric is introduced and examined, which, apart from profit, takes into account more factors after normalization like the Sharpe Ratio, the Maximum Drawdown and the Expected Payoff, as well as a newly introduced Extended Profit Margin factor. Among the automated systems examined and evaluated using the weighted metric, the Adaptive Double Moving Average (Ad2MA) system stands out, followed by the Adaptive Pivot (AdPivot), and the Adaptive Average Directional Index (AdADX) systems. AcknowledgmentsWe would like to thank Dr. Christos Schinas for his time and invaluable guidance towards the methodology of the weighted metric. We would also like to thank Michalis Foulos for the hardware setup and support and Nektarios Mitakidis for his contribution to the representation of the results.This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-02342).

Optimal stop-loss reinsurance with joint utility constraints

<p style='text-indent:20px;'>We investigate the optimal reinsurance problems in this paper, specifically, the stop-loss strategies that can bring mutual benefit to both the insurance company and the reinsurance company. The utility improvement constraints are adopted by both contracting parties to guarantee that a reinsurance contract will bring higher expected utilities of wealth to the two participants. We also introduce five risk criteria that reflect the interests of both parties. Under each optimality criterion, we obtain explicit expressions of optimal stop-loss retentions and the corresponding optimised value of objective functions. The upper and lower bounds of expected utility increments under the optimal stop-loss retentions are provided. In the numerical example, we analyse the expected utility improvements under the criterion of minimising total Value-at-Risk. Notable increases in the lower bound of total utility increments are observed after adopting the joint utility improvement constraints.

Options on a traded account: symmetric treatment of the underlying assets

An option on a traded account is a type of a contract that insures an actively traded portfolio. A holder of the option is free to trade in two or more assets subject to constraints defined by the contract. He keeps resulting trading profits, but he is forgiven any trading loss. Previously studied contracts, such as passport options written on two assets, impose a trading limit on the first asset to be between (a short and a long position) and the residual wealth is invested in the second asset. For passport options, one of the positions in the underlying assets is typically short, making the contract expensive in relationship to the client's wealth as it insures a leveraged portfolio. Our paper presents a version of an insurance of a traded account that treats both underlying assets in a symmetric way. In our approach, we impose a natural symmetric limit in which the agent can fully invest in any underlying asset up to his current wealth and without shorting any asset. This makes the proposed contract relatively cheap and attractive in comparison to the client's initial wealth. In order to preserve the asset symmetry, we use a reference asset that treats both assets equally. We choose a static index that starts with equal weights in the underlying assets. We find an optimal strategy that maximizes the expected payoff of this contract for the two asset case. This strategy has the largest volatility with respect to the index. In order to prove the optimality, we need to generalize Hajek's comparison theorem to the situation of multivariate equations with univariate payoff with an imposed finite boundary condition. The optimal solution leads to a well known stop-loss strategy when all wealth is invested in one asset only, in this case it is the cheaper asset of the two. This trading strategy is interesting on its own as it creates a portfolio with the largest volatility with respect to the index.

한국 주식시장에서 손절매 전략의 활용 효과 : 국민연금기금의 액티브 위험 관리 규정을 중심으로

한국 주식시장에서 손절매 전략의 활용 효과 : 국민연금기금의 액티브 위험 관리 규정을 중심으로

Probability weighting, stop-loss and the disposition effect

In this paper we study a continuous-time, optimal stopping model of an asset sale with prospect theory preferences under pre-commitment. We show for a wide range of value and probability weighting functions, including those of Tversky and Kahneman (1992), that the optimal prospect takes the form of a stop-loss threshold and a distribution over gains. It is skewed with a long right tail. This is consistent with both the widespread use of stop-loss strategies in financial markets, and recent experimental evidence. Moreover, our model with probability weighting in tandem with the S-shaped value function makes predictions for the disposition effect which match in magnitude that calculated by Odean (1998).

Take Profit and Stop Loss Trading Strategies Comparison in Combination with an MACD Trading System

A lot of strategies for Take Profit and Stop Loss functionalities have been propounded and scrutinized over the years. In this paper, we examine various strategies added to a simple MACD automated trading system and used on selected assets from Forex, Metals, Energy, and Cryptocurrencies categories and afterwards, we compare and contrast their results. We conclude that Take Profit strategies based on faster take profit signals on MACD are not better than a simple MACD strategy and of the different Stop Loss strategies based on ATR, the sliding and variable ATR window has the best results for a period of 12 and a multiplier of 6. For the first time, to the best of our knowledge, we implement a combination of an adaptive MACD Expert Advisor that uses back-tested optimized parameters per asset with price levels defined by the ATR indicator, used to set limits for Stop Loss.

The Profitability of Carry Trades: Reality or Illusion?

We carry out a large-scale investigation of the out-of-sample profitability of in-sample profitable carry trade strategies, using foreign exchange data for 48 countries spanning a period from 1983 to 2015 and employing reality check and stepwise tests to correct for data-snooping bias (the factor of luck in model selection). Carry trade strategies chosen as profitable in one period are generally not profitable in an ensuing out-of-sample sample period, especially after correcting for data-snooping, and even after allowing for learning and stop-loss strategies. Any evidence of consistency in carry trade profitability that is found is concentrated in a relatively brief historical period, 2001-2005. We further investigate particular currency pairs that may drive the out-of-sample profitability during this period, and find their performance to be unstable in general. Our findings thus highlight the instability and the factor of luck in generating profits from carry trades.

Selection of Short-term Investment Strategy-Judgment Based on Average Adhesion State

With the development and evolution of the futures market, the prosperity of a country's futures market is more and more important to the country's economic development. In China, the futures market development is not perfect, futures product structure still need to improve. Futures market, the existence of many speculators, futures laws and regulations imperfect, and the development of economic globalization, are the futures investment is facing many uncertain risks. In the choice of investment strategy, more and more investors to take prudent investment strategy, which, a large part of the investors tend to technical analysis. Through technical analysis, traders judge market trends and follow the cyclical changes in the trend to make stock and all financial derivatives trading decisions. In the technical analysis, the average system analysis is the most commonly used in practice is the highest accuracy of the analytical technology, this paper through the copper, rubber, sugar, cotton, zinc, several representative commodity futures to study, to verify the average bonding State of investment strategy choice. First, this paper determines the condition of the bonding state by calculating the distance of different mean points. Then, it is judged whether the reversal of the form is long or short by the comparison of the MA value of the different mean points. Finally, the factory strategy and the factory strategy are determined. And to take more and short after the operation of the proposed stop-loss strategy. The effect of these strategies is very significant, the sum of several commodity futures net profit of 113744.79 yuan, profit and loss ratio of 2.82.

Stop-loss strategies with serial correlation, regime switching, and transaction costs

Stop-loss strategies are commonly used by investors to reduce their holdings in risky assets if prices or total wealth breach certain pre-specified thresholds. We derive closed-form expressions for the impact of stop-loss strategies on asset returns that are serially correlated, regime switching, and subject to transaction costs. When applied to a large sample of individual U.S. stocks, we show that tight stop-loss strategies tend to underperform the buy-and-hold policy in a mean-variance framework due to excessive trading costs. Outperformance is possible for stocks with sufficiently high serial correlation in returns. Certain strategies succeed at reducing downside risk, but not substantially.