scholarly journals Beating the market? A mathematical puzzle for market efficiency

2021 ◽  
Author(s):  
Michael Heinrich Baumann
Keyword(s):  
Efficient Market Hypothesis ◽  
Specific Model ◽  
Trading Strategies ◽  
Specific Class ◽  
Economic Literature ◽  
Model Free ◽  
Expected Gain ◽  
Time Varying Parameters ◽  
Price Trends ◽  
Bid Ask Spreads

AbstractThe efficient market hypothesis is highly discussed in economic literature. In its strongest form, it states that there are no price trends. When weakening the non-trending assumption to arbitrary short, small, and fully unknown trends, we mathematically prove for a specific class of control-based trading strategies positive expected gains. These strategies are model free, i.e., a trader neither has to think about predictable patterns nor has to estimate market parameters such as the trend’s sign like momentum traders have to do. That means, since the trader does not have to know any trend, even trends too small to find are enough to beat the market. Adjustments for risk and comparisons with buy-and-hold strategies do not satisfactorily solve the problem. In detail, we generalize results from the literature on control-based trading strategies to market settings without specific model assumptions, but with time-varying parameters in discrete and continuous time. We give closed-form formulae for the expected gain as well as the gain’s variance and generalize control-based trading rules to a setting where older information counts less. In addition, we perform an exemplary backtesting study taking transaction costs and bid-ask spreads into account and still observe—on average—positive gains.

Cluster-Based Input Selection for Transparant Fuzzy Modeling

2008 ◽  
pp. 1138-1156
Author(s):  
Can Yang ◽  
Jun Meng ◽  
Shanan Zhu
Keyword(s):  
Computational Cost ◽  
Fuzzy Model ◽  
Selection Procedure ◽  
Specific Model ◽  
Benchmark Problems ◽  
Input Selection ◽  
Model Free ◽  
Interpretable Model ◽  
Data Collections ◽  
Accuracy Of Prediction

Input selection is an important step in nonlinear regression modeling. By input selection, an interpretable model can be built with less computational cost. Input selection thus has drawn great attention in recent years. However, most available input selection methods are model-based. In this case, the input data selection is insensitive to changes. In this paper, an effective model-free method is proposed for the input selection. This method is based on sensitivity analysis using Minimum Cluster Volume (MCV) algorithm. The advantage of our proposed method is that with no specific model needed to be built in advance for checking possible input combinations, the computational cost is reduced and changes of data patterns can be captured automatically. The effectiveness of the proposed method is evaluated by using three well-known benchmark problems, which show that the proposed method works effectively with small and medium sized data collections. With an input selection procedure, a concise fuzzy model is constructed with high accuracy of prediction and better interpretation of data, which serves the purpose of patterns discovery in data mining well.

scholarly journals Forecasting Bitcoin Trends Using Algorithmic Learning Systems

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 838
Author(s):  
Gil Cohen
Keyword(s):  
Linear Regression ◽  
Efficient Market Hypothesis ◽  
Swarm Optimization ◽  
Long Period ◽  
Forecasting Methods ◽  
Price Trends ◽  
Regression Techniques ◽  
Forecasting System ◽  
Algorithmic Learning ◽  
Daily Returns

This research has examined the ability of two forecasting methods to forecast Bitcoin’s price trends. The research is based on Bitcoin—USA dollar prices from the beginning of 2012 until the end of March 2020. Such a long period of time that includes volatile periods with strong up and downtrends introduces challenges to any forecasting system. We use particle swarm optimization to find the best forecasting combinations of setups. Results show that Bitcoin’s price changes do not follow the “Random Walk” efficient market hypothesis and that both Darvas Box and Linear Regression techniques can help traders to predict the bitcoin’s price trends. We also find that both methodologies work better predicting an uptrend than a downtrend. The best setup for the Darvas Box strategy is six days of formation. A Darvas box uptrend signal was found efficient predicting four sequential daily returns while a downtrend signal faded after two days on average. The best setup for the Linear Regression model is 42 days with 1 standard deviation.

scholarly journals A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets

2019 ◽  
Vol 12 (2) ◽  
pp. 67 ◽  
Author(s):  
Kyriazis
Keyword(s):  
Long Memory ◽  
Detrended Fluctuation Analysis ◽  
Efficient Market Hypothesis ◽  
Trading Strategies ◽  
Fluctuation Analysis ◽  
Efficient Market ◽  
Systematic Survey ◽  
Rescaled Range ◽  
Pricing Behavior ◽  
Detrended Fluctuation

This study conducts a systematic survey on whether the pricing behavior of cryptocurrencies is predictable. Thus, the Efficient Market Hypothesis is rejected and speculation is feasible via trading. We center interest on the Rescaled Range (R/S) and Detrended Fluctuation Analysis (DFA) as well as other relevant methodologies of testing long memory in returns and volatility. It is found that the majority of academic papers provides evidence for inefficiency of Bitcoin and other digital currencies of primary importance. Nevertheless, large steps towards efficiency in cryptocurrencies have been traced during the last years. This can lead to less profitable trading strategies for speculators.

scholarly journals Generation of predictive price and trading volume patterns in a model of dynamically evolving free market supply and demand

2001 ◽  
Vol 6 (1) ◽  
pp. 37-47 ◽  
Author(s):  
J. K. Wang
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Free Market ◽  
Supply And Demand ◽  
Market Price ◽  
Efficient Market Hypothesis ◽  
Apparent Contradiction ◽  
Equity Values ◽  
Price Trends ◽  
Price History

I present a model of stock market price fluctuations incorporating effects of share supply as a history-dependent function of previous purchases and share demand as a function of price deviation from moving averages. Price charts generated show intervals of oscillations switching amplitude and frequency suddenly in time, forming price and trading volume patterns well-known in market technical analysis. Ultimate price trends agree with traditional predictions for specific patterns. The consideration of dynamically evolving supply and demand in this model resolves the apparent contradiction with the Efficient Market Hypothesis: perceptions of imprecise equity values by a world of investors evolve over non-negligible periods of time, with dependence on price history.

Profitability of Trading Strategies Before and During the Greek Crisis: An Empirical Study.

2017 ◽  
Vol 11 (1) ◽  
pp. 1-26
Author(s):  
Efstathios Xanthopoulos ◽  
Konstantinos Aravossis ◽  
Spyros Papathanasiou
Keyword(s):  
Stock Exchange ◽  
Statistical Tests ◽  
Moving Average ◽  
Efficient Market Hypothesis ◽  
Trading Strategies ◽  
Technical Trading ◽  
Technical Trading Rules ◽  
Trading Rules ◽  
Greek Crisis ◽  
Athens Stock Exchange

This paper investigates the profitability of technical trading rules in the Athens Stock Exchange (ASE), utilizing the FTSE Large Capitalization index over the seven-year period 2005-2012, which was before and during the Greek crisis. The technical rules that will be explored are the simple moving average, the envelope (parallel bands) and the slope (regression). We compare technical trading strategies in the spirit of Brock, Lakonishok, and LeBaron (1992), employing traditional t-test and Bootstrap methodology under the Random Walk with drift, AR(1) and GARCH(1,1) models. We enrich our analysis via Fourier analysis technique (FFT) and more statistical tests. The results provide strong evidence on the profitability of the examined technical trading rules, even during recession period (2009-2012), and contradict the Efficient Market Hypothesis.

scholarly journals Trading strategy using share buybacks: evidence from India

2020 ◽  
Vol 17 (2) ◽  
pp. 169-182
Author(s):  
Asheesh Pandey ◽  
Vandana Bhama ◽  
Amiya Kumar Mohapatra
Keyword(s):  
Insider Trading ◽  
Abnormal Returns ◽  
Significant Risk ◽  
Efficient Market Hypothesis ◽  
Trading Strategy ◽  
Trading Strategies ◽  
New Delhi ◽  
Event Window ◽  
Infrastructural Support ◽  
Available Information

The efficient market hypothesis states that in the efficient markets, participants cannot make extra-normal returns by exploiting any publicly available information. However, traders are constantly looking to exploit publicly available information to generate abnormal returns for themselves and their clients. One such event is share buyback announcement, which traders can utilize to create profitable trading strategies. The authors undertake the present study to examine if share buyback announcements provide profitable trading strategies to traders. Event study methodology has been adopted to analyze buyback announcements by Indian companies from January 2012 to December 2018. Forty-one (41) day window period comprising of 20 days pre-event, an announcement day, and 20 days post-event period is created to analyze the risk-adjusted average abnormal returns. The empirical findings suggest that there are negligible trading opportunities available for investors post announcements. However, significant risk-adjusted returns are found in the pre-event window, indicating that if investors can predict buyback announcements, they may earn extra-normal returns. The study confirms that Indian stock markets are in the semi-strong form of efficiency. The study also provides a profitable trading strategy for investors in the pre-event window. Finally, it also draws the regulators’ attention to see if insider trading could be the reason for abnormal returns in the pre-event window. The authors conclude the results by confirming that Indian markets are semi-strong in market efficiency and by indicating regulatory interventions to control insider trading. AcknowledgementThe infrastructural support provided by FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.

Cluster-Based Input Selection for Transparant Fuzzy Modeling

2011 ◽  
pp. 826-845
Author(s):  
Can Yang ◽  
Jun Meng ◽  
Shanan Zhu
Keyword(s):  
Computational Cost ◽  
Fuzzy Model ◽  
Selection Procedure ◽  
Specific Model ◽  
Benchmark Problems ◽  
Input Selection ◽  
Model Free ◽  
Interpretable Model ◽  
Data Collections ◽  
Accuracy Of Prediction

Input selection is an important step in nonlinear regression modeling. By input selection, an interpretable model can be built with less computational cost. Input selection thus has drawn great attention in recent years. However, most available input selection methods are model-based. In this case, the input data selection is insensitive to changes. In this paper, an effective model-free method is proposed for the input selection. This method is based on sensitivity analysis using Minimum Cluster Volume (MCV) algorithm. The advantage of our proposed method is that with no specific model needed to be built in advance for checking possible input combinations, the computational cost is reduced and changes of data patterns can be captured automatically. The effectiveness of the proposed method is evaluated by using three well-known benchmark problems, which show that the proposed method works effectively with small and medium sized data collections. With an input selection procedure, a concise fuzzy model is constructed with high accuracy of prediction and better interpretation of data, which serves the purpose of patterns discovery in data mining well.

scholarly journals Rapid Model-Free State of Health Estimation for End-Of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2597
Author(s):  
Alireza Rastegarpanah ◽  
Jamie Hathaway ◽  
Rustam Stolkin
Keyword(s):  
Neural Network ◽  
Impedance Spectroscopy ◽  
Second Life ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Specific Model ◽  
State Of Health ◽  
Model Based ◽  
Model Free ◽  
Estimation Scheme

The continually expanding number of electric vehicles in circulation presents challenges in terms of end-of-life disposal, driving interest in the reuse of batteries for second-life applications. A key aspect of battery reuse is the quantification of the relative battery condition or state of health (SoH), to inform the subsequent battery application and to match batteries of similar capacity. Impedance spectroscopy has demonstrated potential for estimation of state of health, however, there is difficulty in interpreting results to estimate state of health reliably. This study proposes a model-free, convolutional-neural-network-based estimation scheme for the state of health of high-power lithium-ion batteries based on a dataset of impedance spectroscopy measurements from 13 end-of-first-life Nissan Leaf 2011 battery modules. As a baseline, this is compared with our previous approach, where parameters from a Randles equivalent circuit model (ECM) with and without dataset-specific adaptations to the ECM were extracted from the dataset to train a deep neural network refined using Bayesian hyperparameter optimisation. It is demonstrated that for a small dataset of 128 samples, the proposed method achieves good discrimination of high and low state of health batteries and superior prediction accuracy to the model-based approach by RMS error (1.974 SoH%) and peak error (4.935 SoH%) metrics without dataset-specific model adaptations to improve fit quality. This is accomplished while maintaining the competitive performance of the previous model-based approach when compared with previously proposed SoH estimation schemes.

The profitability of trading strategies from sharia-compliant and conventional banks in the GCC: a comparative study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hesham I. AlMujamed
Keyword(s):  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Trading Strategy ◽  
Small Sample ◽  
Statistical Analyses ◽  
Trading Strategies ◽  
Accounting Disclosure ◽  
Content Type ◽  
Conventional Banks ◽  
Filter Rules

Purpose The purpose of this research was to examine the effectiveness of filter rules and investigate the weak form of the efficient market hypothesis (EMH) on sample shares of shariah-compliant vs. conventional banks listed on the Gulf Cooperation Council (GCC) stock market. Design/methodology/approach Nine trading filter strategies with different statistical analyses were used as defined in the literature (Fifield et al., 2005; Almujamed et al., 2018). Daily closing equity prices of a sample of twenty shariah-compliant banks and twenty conventional banks were recorded over the 18-year period ending 31 December 2017. Findings Shares of shariah-compliant banks in the GCC were not weak-form efficient since trading based on past information was predictable, profitable and outperformed the corresponding naïve buy-and-hold trading strategy. Shares of conventional GCC banks underperformed Research limitations/implications This paper’s findings should be useful for central banks and capital market authorities in GCC countries for evaluation when considering new regulations or process changes. Limitations include small sample numbers and need for more recent evaluations of accounting disclosure levels. A wider range of data, statistical analyses and other trading strategies is needed. Potential investors (Muslim and non-Muslim), shariah supervisory boards, and preparers of financial statements can benefit from this study. Practical implications The results suggest that selection of trading strategy affects the success of the rule and that mid-sized filters are the best. Originality/value This is an innovative study comparing performance of shariah-compliant and conventional banks under different filter rules.

scholarly journals Data-Driven Model-Free Adaptive Control Based on Error Minimized Regularized Online Sequential Extreme Learning Machine

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3241 ◽  
Author(s):  
Xiaofei Zhang ◽  
Hongbin Ma
Keyword(s):  
Adaptive Control ◽  
Extreme Learning Machine ◽  
Discrete Time ◽  
Control Method ◽  
Time Varying ◽  
Varying Parameters ◽  
Model Free ◽  
Time Varying Parameters ◽  
Learning Machine ◽  
Linearized Model

Model-free adaptive control (MFAC) builds a virtual equivalent dynamic linearized model by using a dynamic linearization technique. The virtual equivalent dynamic linearized model contains some time-varying parameters, time-varying parameters usually include high nonlinearity implicitly, and the performance will degrade if the nonlinearity of these time-varying parameters is high. In this paper, first, a novel learning algorithm named error minimized regularized online sequential extreme learning machine (EMREOS-ELM) is investigated. Second, EMREOS-ELM is used to estimate those time-varying parameters, a model-free adaptive control method based on EMREOS-ELM is introduced for single-input single-output unknown discrete-time nonlinear systems, and the stability of the proposed algorithm is guaranteed by theoretical analysis. Finally, the proposed algorithm is compared with five other control algorithms for an unknown discrete-time nonlinear system, and simulation results show that the proposed algorithm can improve the performance of control systems.

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