Simple Trading Strategy Research Articles

Regime‐dependent commodity price dynamics: A predictive analysis

AbstractWe develop an econometric modelling framework to forecast commodity prices taking into account potentially different dynamics and linkages existing at different states of the world and using different performance measures to validate the predictions. We assess the extent to which the quality of the forecasts can be improved by entertaining different regime‐dependent threshold models considering different threshold variables. We evaluate prediction quality using both loss minimization and profit maximization measures based on directional accuracy, directional value, the ability to predict turning points, and the returns implied by a simple trading strategy. Our analysis provides overwhelming evidence that allowing for regime‐dependent dynamics leads to improvements in predictive ability for the Goldman Sachs Commodity Index, as well as for its five sub‐indices (energy, industrial metals, precious metals, agriculture, and livestock). Our results suggest the existence of a trade‐off between predictive ability based on loss and profit measures, which implies that the particular aim of the prediction exercise carried out plays a very important role in terms of defining which set of models is the best to use.

How to select oil price prediction models — The effect of statistical and financial performance metrics and sentiment scores

Predicting crude oil prices is an important yet challenging forecasting problem due to various influencing quantitative and qualitative factors. To address the growing number of potential prediction models and model parameters to consider during model selection, we highlight the need to systematically compare alternative prediction models and variables while taking the specific context of their application into account. Specifically, we provide a novel perspective on oil price prediction models by comparing a variety of different forecasting models and considering both their statistical and financial performance. In addition to common statistical measures, to assess the usefulness in a practical setting we evaluate the potential financial impact of the predictions in a simulation of a simple trading strategy. We show that the ranking of different approaches depends on the selected evaluation metric and that small differences between models in one evaluation metric can translate into large differences in another metric. For instance, forecasts that are not considered statistically different can lead to substantially different financial performance when the forecasts are used in a trading strategy. Finally, we show that including qualitative information in the prediction models through sentiment analysis can yield both statistical and financial performance improvements.

Comparison between transformer, informer, autoformer and non-stationary transformer in financial market

This paper delves into the significance of predicting stock prices and carries out comparative experiments using a variety of models, including Support Vector Regression, Long Short-Term Memory model, Transformer, Informer, Autoformer, and Non-Stationary Transformer. These models are used to train and forecast the China Securities Index, Hang Seng Index, and S&P 500 Index. The results of the experiments are measured using indicators such as Mean Absolute Error and Root Mean Square Error. The findings show that the Non-Stationary Transformer model has the highest prediction accuracy. Additionally, a simple trading strategy is designed for each model and their Sharpe and Calmar ratios are compared. Since Autoformer has the highest Sharpe and Calmar, it can be concluded that Autoformer is the most practical in financial market among the four models. This research contributes to the field of stock price prediction by providing an empirical study on the application of Transformer and its derivative models which have been less explored in this domain. In conclusion, this paper offers valuable insights and recommendations for data scientists and financial engineer and introduces new methods for predicting stock prices.

Technical analysis as a sentiment barometer and the cross-section of stock returns

This paper explores an unexamined sentiment channel through which technical analysis can add value. We use a spectrum of technical trading strategies to build a daily market sentiment indicator that is highly correlated with other commonly used sentiment measures. This technical-analysis-based sentiment indicator positively predicts near-term returns and is inversely related to long-term returns in the cross-section. Simple trading strategies based on this sentiment indicator yield substantial abnormal returns. These results are consistent with the explanation that lack of synchronization induces rational arbitrageurs to exploit the mispricing before it is corrected.

Asset price bubbles, wealth preserving, dominating, and replicating trading strategies

This paper studies an arbitrage-free, competitive and frictionless market with trading in a single risky asset and money market account, where the risky asset exhibits a price bubble. We analyze two sets of self-financing and admissible trading strategies in this market. The first are simple trading strategies, and the second are trading strategies that replicate or super-replicate the risky asset's payoffs at the model's horizon. We show that in both sets there exist wealth preserving trading strategies, i.e. trading strategies whose initial value equals the present value of its future cash flows. And, in the second set, we show that there are wealth preserving replicating trading strategies that dominate buying and holding the risky asset. The practical applications of these insights are discussed.

주식시장국면 예측과 투자전략에 대한 연구

Purpose - This study investigates the performance of investment strategies incorporating estimated stock market cycle based on a lead-lag relationship between business cycle and stock market cycle, thereby deriving empirical implications on risk management.
 Design/methodology/approach - The data period ranges from June 1953 to September 2022 and de-trended short rate, term spread, credit spread, stock market volatility are considered as major input variables to estimate business cycle and stock market cycle by applying probit model. Based on the estimated stock market cycle, two types of strategies are constructed and their performance relative to the benchmark is empirically examined.
 Findings Two types of strategies based on stock market cycle are considered: The first strategy is to long(short) on stocks when stock market stage is expected to be an expansion(a recession), and the second one is to long on stocks(bonds) when expecting an expansion(a recession). The empirical results show that the strategies based on stock market cycle outperforms a simple buy and hold strategy in both in-sample and out-of-sample investigation. Also the out-of-sample evidence suggests that the second strategy which is in line with asset allocation is more profitable than the first one.
 Research implications or Originality The strategies considered in this study are based on the estimated stock market cycle which only depends on a few easily available financial variables, thereby making easier to establish such a strategy. It implies that investors enhance investment performance by constructing a relatively simple trading strategies if they set their position on stocks or choose which asset class to buy conditioning on stock market cycle.

Deep Learning-Based Methods for Forecasting Brent Crude Oil Return Considering COVID-19 Pandemic Effect

Forecasting return and profit is a primary challenge for financial practitioners and an even more critical issue when it comes to forecasting energy market returns. This research attempts to propose an effective method to predict the Brent Crude Oil return, which results in remarkable performance compared with the well-known models in the return prediction. The proposed hybrid model is based on long short-term memory (LSTM) and convolutional neural network (CNN) networks where the autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedasticity (GARCH) outputs are used as features, along with return lags, price, and macroeconomic variables to train the models, resulting in significant improvement in the model’s performance. According to the obtained results, our proposed model performs better than other models, including artificial neural network (ANN), principal component analysis (PCA)-ANN, LSTM, and CNN. We show the efficiency of our proposed model by testing it with a simple trading strategy, indicating that the cumulative profit obtained from trading with the prediction results of the proposed 2D CNN-LSTM model is higher than those of the other models presented in this research. In the second part of this study, we consider the spread of COVID-19 and its impact on the financial markets to present a precise LSTM model that can reflect the impact of this disease on the Brent Crude Oil return. This paper uses the significance test and correlation measures to show the similarity between the series of Brent Crude Oil during the SARS and the COVID-19 pandemics, after which the data during the SARS period are used along with the data during COVID-19 to train the LSTM. The results demonstrate that the proposed LSTM model, tuned by the SARS data, can better predict the Brent Crude Oil return during the COVID-19 pandemic.

Trading time seasonality in electricity futures

Trading time seasonality reflects the seasonal behavior of futures prices with the same time of maturity. Hence, it differs from classical seasonality, which reflects seasonal behavior induced by the spot price observed for varying maturities. This type of seasonality is linked to the pricing kernel which in turn accounts for seasonal changes in preferences of agents and tied to risk aversion and thus the demand for hedging. In the present study we empirically examine trading time seasonality in yearly Nordic and German electricity futures contracts. Visual inspection of both average monthly futures prices and the futures backward curves provides strong indications of futures prices systematically varying over the trading year. On average both Nordic and German futures prices are lowest in first quarter and highest in third quarter trading months. This is confirmed by statistical tests of stochastic dominance. Exploiting this insight in a simple trading strategy induces positive and significant alphas in the sense of the capital asset pricing model. We relate the findings to potential seasonal risk preferences and hedging pressure in the electricity futures market.

Corporate Investment and Stock Return Momentum

We investigate the link between corporate investment and the momentum effect in stock returns. We argue that the momentum effect in a firm’s stock returns tends to be generated as a result of a series of information exchanges between stock market investors and firm insiders regarding the firm’s investment opportunities. Our theoretical setup predicts that past winners (losers) are likely to increase (decrease) their net investment, and the more they invest (disinvest) the more likely they are to realize positive (negative) returns in subsequent periods. Our empirical tests using a large sample of firms in 1984–2017 provide support for our hypotheses. We further design a momentum trading strategy, which buys past winners with positive predicted changes in net investment and sells past losers with negative predicted changes in net investment, and demonstrate that this trading strategy generates superior returns compared to a simple momentum trading strategy.

Efficient Spread Betting Markets: A Literature Review

Are simple trading strategies profitable? It is a question that has been on the minds of academics and practitioners for decades. In this paper, we review the longstanding literature on trading strategies in spread betting (also known as handicap betting), a popular sports betting microstructure. We review over 600 strategy implementations and find that market efficiency and systematic misperceptions are not mutually exclusive per se. Predictable glitches occur, but they are too small to be profitably exploited which is consistent with efficient markets. Furthermore, while controlling for data mining issues is becoming mainstream in finance, it has not yet made its way into this literature. We provide evidence that the hurdle rate of | z| > 3 which has been put forward in the broader finance literature should also be used in betting market research.

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.

Profiting on the Stock Market in Pandemic Times: Study of COVID-19 Effects on CESEE Stock Markets

This research deals with stock market reactions of Central Eastern and South Eastern European (CESEE) markets to the COVID-19 pandemic, via the event study methodology approach. Since the stock markets react quickly to certain announcements, the used methodology is appropriate to evaluate how the aforementioned markets reacted to certain events. The purpose of this research was to evaluate possibilities of obtaining profits on the stock markets during great turbulences, when a majority of the participants panic. More specifically, the contrarian trading strategies are observed if they can obtain gains, although a majority of the markets suffer great losses during pandemic shocks. The contributions to the existing literature of this research are as follows. Firstly, empirical research on CESEE stock markets regarding other relevant topics is still scarce and should be explored more. Secondly, the event study approach of COVID-19 effects utilized in this study has (to the knowledge of the author) not yet been explored on the aforementioned markets. Thirdly, based on the results of CESEE market reactions to specific announcements regarding COVID-19, a simulation of simple trading strategies will be made in order to estimate whether some investors could have profited in certain periods. The results of the study indicate promising results in terms of exploiting other investors’ panicking during the greatest decline of stock market indices. Namely, the initial results, as expected, indicate strong negative effects of specific COVID-19 announcements on the selected stock markets. Secondly, the obtained information was shown to be useful for contrarian strategy in order to exploit great dips in the stock market indices values.

Using company-specific headlines and convolutional neural networks to predict stock fluctuations

This work presents a convolutional neural network for the prediction of next-day stock fluctuations using company-specific news headlines. Experiments to evaluate model performance using various configurations of word embeddings and convolutional filter widths are reported. The total number of convolutional filters used is far fewer than is common, reducing the dimensionality of the task without loss of accuracy. Furthermore, multiple hidden layers with decreasing dimensionality are employed. A classification accuracy of 61.7% is achieved using pre-learned embeddings, that are fine-tuned during training to represent the specific context of this task. Multiple filter widths are also implemented to detect different length phrases that are key for classification. Trading simulations are conducted using the presented classification results. Initial investments are more than tripled over an 838-day testing period using the optimal classification configuration and a simple trading strategy. Two novel methods are presented to reduce the risk of the trading simulations. Adjustment of the sigmoid class threshold and re-labelling headlines using multiple classes form the basis of these methods. A combination of these approaches is found to be more than double the Average Trade Profit achieved during baseline simulations.

Does the Federal Open Market Committee cycle affect credit risk?

AbstractThis paper studies the returns of credit default swap (CDS) indices over the Federal Open Market Committee (FOMC) cycle. We document that the CDS return is significantly higher in even weeks than in odd weeks of the FOMC cycle. The biweekly pattern in the CDS market is not a mere reflection of that in the stock market. A simple trading strategy based on the biweekly pattern yields an annual excess return of 8.8%. This pattern is linked to the resolution of macroeconomic uncertainty by the biweekly schedules of the Fed Reserve internal Board of Governors meetings. We provide further evidence that the Fed affects the CDS market via unexpected information signals and monetary policies that lead to reductions in the risk premium.

The Natural Gas Announcement Day Puzzle

This paper studies natural gas futures returns on EIA storage announcement days.More than 50% of the annual return is earned on these days. We find a significant difference between announcement and non-announcement day returns, which cannot be explained by the announcement surprise or other control variables. At the intraday level, the return splits half into a pre- and post-announcement part. The pre-announcement return is entirely generated on days when storage levels exceed analysts’ expectations casting doubt on explanations based on informed trading.After transaction and funding cost, a simple trading strategy yields substantial returns.Gas Markets, Announcement Effect, Storage News, Intraday

Do accounting information and market environment matter for cross‐asset predictability?

AbstractThis paper examines whether the differences in accounting information between stocks affect cross‐asset return predictability. We use a comprehensive set of accounting variables and find that abnormal accruals, earnings smoothness, book‐to‐market, firm age, leverage, abnormal capital investment and investment growth, among others, explain the variation in return predictability across pairing stocks. Moreover, our results show that cross‐asset predictability varies over time and is associated with funding liquidity and market sentiment. A simple trading strategy based on our findings yields a higher mean return, lower standard deviation and higher Sharpe ratio compared to a buy‐and‐hold strategy.

Does the Croatian Stock Market Have Seasonal Affective Disorder?

This paper explores mood anomalies, specifically the seasonal affective disorder (SAD) effect on the Zagreb Stock Exchange (ZSE). SAD is defined as a syndrome of depressive episodes in human behavior due to the changing of the season. Thus, the motive of this research is to gain better insights into the investors’ sentiment regarding SAD effects. The purpose of the research is to observe how investors’ sentiment affects the return and risk series on ZSE and if this could be exploitable. Using daily data on stock market return CROBEX for the period January 2010—February 2021, SAD effects are tested to explore if seasonal changes affect the stock returns and risk. Besides the SAD variable in the model, some control variables are included as well: Monday, tax, and COVID-19 effect. The results indicate that SAD effects exist on ZSE, even with controlling for mentioned effects; and asymmetries around winter solstice exist. Implications of such findings can be found in simulating trading strategies, which could incorporate such information to gain profits. Limitations of the research focus on one market, observing static parameters of the estimated models, and observing simple trading strategies. Thus, future research should focus on international diversification possibilities, time-varying models, and fully exploring the exploitation possibilities of such findings.

On cointegration and cryptocurrency dynamics

This paper aims to model the joint dynamics of cryptocurrencies in a nonstationary setting. In particular, we analyze the role of cointegration relationships within a large system of cryptocurrencies in a vector error correction model (VECM) framework. To enable analysis in a dynamic setting, we propose the COINtensity VECM, a nonlinear VECM specification accounting for a varying systemwide cointegration exposure. Our results show that cryptocurrencies are indeed cointegrated with a cointegration rank of four. We also find that all currencies are affected by these long term equilibrium relations. The nonlinearity in the error adjustment turned out to be stronger during the height of the cryptocurrency bubble. A simple statistical arbitrage trading strategy is proposed showing a great in-sample performance, whereas an out-of-sample analysis gives reason to treat the strategy with caution.

Application of Improved LSTM Algorithm in Macroeconomic Forecasting.

From a macro perspective, futures index of agricultural products can reflect the trend of macroeconomy and can also have an early warning effect on the possible crisis and provide a reference for the government's economic forecast and macro control. Therefore, it is necessary to strengthen the research on early warning and prediction of agricultural futures price. For the prediction of futures price, there are two kinds of common models: one is the traditional classic time series model, and the other is the neural network model under the wave of artificial intelligence. This paper selects the 1976 closing data of agricultural futures index from January 10, 2012, to February 27, 2020, and uses the time series differential autoregressive integrated moving average model (ARIMA model) and long short-term memory model (LSTM model) to study this work, respectively, and compares the predicted effects of the two models in some metrics. Based on the predicted results of the two models, a simple trading strategy is established, and the trading effects of the two models are compared. The results show that the LSTM model has obvious advantage over ARIMA time series model in the price index prediction of agricultural futures market.

Do Accounting Information and Market Environment Matter for Cross-Asset Predictability?

This paper examines whether the differences in accounting information between stocks affect cross-asset return predictability. We use a comprehensive set of accounting variables and find that abnormal accruals, earnings smoothness, book-to-market, firm age, leverage, abnormal capital investment, and investment growth, among others, explain the variation in return predictability across pairing stocks. Moreover, our results show that cross-asset predictability varies over time and is associated with funding liquidity and market sentiment. A simple trading strategy based on our findings yields a higher mean return, lower standard deviation, and higher Sharpe ratio compared to the buy-and-hold strategy.