scholarly journals Is It Possible to Earn Abnormal Return in an Inefficient Market? An Approach Based on Machine Learning in Stock Trading

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bui Thanh Khoa ◽  
Tran Trong Huynh
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Abnormal Returns ◽  
Weak Form ◽  
Support Vector ◽  
Investment Efficiency ◽  
Short Term ◽  
Stock Trading ◽  
Efficient Market ◽  
Modern Approach

Risk management and stock investment decision-making is an essential topic for investors and fund managers, especially in the context of the COVID-19 pandemic. The problem becomes easier if the market is efficient, where stock prices fully reflect potential risk. Nevertheless, if the market is not efficient, investors may have an opportunity to find an effective investment method. Vietnam is one of the emerging markets; the efficiency is still weak. Thus, there will be an opportunity for astute investors. This study aims to test the weak-form efficient market and provide a modern approach to investors’ decision-making. To achieve that aim, this study uses historical data of stocks in the VN-Index and VN30 portfolio to buy and sell within a one-day period under the rolling window approach to test the Ho Chi Minh City Stock Exchange (HoSE) through a runs test and to perform stock trading using the support vector machine (SVM) and logistic regression. The buying/selling of stocks is guided by the forecasted outcomes (increase/decrease) of logistic regression and SVM. This study adjusted the return rate in proportion to the risks and compared it with index investments of VN-Index and VN30 to evaluate investment efficiency. The test results dismissed the weak-form efficient-market hypothesis, which opens up many opportunities for short-term traders. This study’s primary contribution is to provide a stock trading strategy for short-term investors to maximize trading profits. Because logistic regression and SVM have proven effective trading methods, investors can use them to achieve abnormal returns.

An Empirical Study on Efficient Market Hypothesis with reference to FMCG Sector

GIS Business ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 109-126
Author(s):  
Nitin Tanted ◽  
Prashant Mistry
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Abnormal Returns ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Controversial Issues ◽  
Efficient Market ◽  
Price Movements ◽  
Run Test ◽  
Stock Price Movements

One of the highly controversial issues in the area of finance is “Efficient Market Hypothesis”. Efficient Market Hypothesis states that, “In an efficient market, all available price information is reflected in the stock prices and it is not possible to generate abnormal returns compared to other investors.” A lot of studies conducted previouslyto test the Efficient Market Hypothesis, confirmed the theory until recent years, when some academicians found it to be non-applicable in financial markets. According to them, it is possible to forecast the stock price movements using Technical Analysis. The results of various studies have been inconclusive and indefinite about the issue. This study attempted to test the efficiency of FMCG Sector stocks in India in its weak form. For the study, closing prices of top 10 stocks from Nifty FMCG index has been taken for the 5-year period ranging from 1st October 2014 to 30th September 2019. Wald-Wolfowitz Run test has been used to test the haphazard movements in the stock price movements. The results indicated that FMCG sector stocks does support the Efficient Market Hypothesis and exhibit efficiency in its weak form. Hence, it is not possible to accurately predict the price movements of these stocks.

Testing the weak form of efficient market hypothesis for socially responsible and Shariah indexes in the USA

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abbas Khan ◽  
Muhammad Yar Khan ◽  
Abdul Qayyum Khan ◽  
Majid Jamal Khan ◽  
Zia Ur Rahman
Keyword(s):  
Stock Prices ◽  
Arima Model ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Future Price ◽  
Short Term ◽  
Socially Responsible ◽  
Efficient Market ◽  
Content Type ◽  
The Us

Purpose By testing the weak form of efficient market hypothesis (EMH) this study aims to forecast the short-term stock prices of the US Dow and Jones environmental socially responsible index (SRI) and Shariah compliance index (SCI). Design/methodology/approach This study checks the validity of the weak form of EMH for both SCI and SRI prices by using different parametric and non-parametric tests, i.e. augmented Dickey-Fuller test, Philip-Perron test, runs test and variance ratio test. If the EMH is invalid, the research further forecasts short-term stock prices by applying autoregressive integrated moving average (ARIMA) model using daily price data from 2010 to 2018. Findings The research confirms that a weak form of EMH is not valid in the US SRI and SCI. The historical data can predict short-term future price movements by using technical ARIMA model. Research limitations/implications This study provides better guidance to risk-averse national and international investors to earn higher returns in the US SRI and SCI. This study can be extended to test the EMH of Islamic equity in the Middle East and North Africa region and other top Islamic indexes in the world. Originality/value This study is a new addition to the existing literature of equity investment and price forecasting by comparing and investigating the market efficiency of two interrelated US SRI and SCI.

An Efficient Decision-Making Approach for Short Term Indoor Room Temperature Forecasting in Smart Environment: Evidence from India

2021 ◽  
pp. 1-42
Author(s):  
Kamal Pandey ◽  
Bhaskar Basu ◽  
Sandipan Karmakar
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Room Temperature ◽  
Linear Models ◽  
Comfort Level ◽  
Support Vector ◽  
Nonlinear Phenomenon ◽  
Short Term ◽  
Smart Buildings ◽  
Temperature Forecasting

“Smart cities” start with “Smart Buildings” that improve the quality of urban services while ensuring sustainability. The current scenario in India reveals that the corporate and residential building structures are incorporating various self-sustainable techniques. Out of the multiple factors governing the comfort of smart buildings, indoor room temperature is an important one, since it drives the need of cooling or heating through controlling systems. Around one-third of total energy consumption of commercial buildings in India is attributed to Heating, Ventilation and Air Conditioning (HVAC) systems. Accurate prediction of indoor room temperature helps in creating an efficient equilibrium between energy consumption and comfort level of the building, thus providing opportunities for efficient decision making for energy optimization. Considering Indian climatic and geographical conditions, this paper proposes an efficient decision making approach using Bayesian Dynamic Models (BDM) for short-term indoor room temperature forecasting of a corporate building structure. The results obtained from Bayesian Dynamic linear model, using Expectation Maximization (EM) algorithm, have been compared to standard Auto Regressive Integrated Moving Average (ARIMA) model, and have been found to be more accurate. Forecasting of indoor room temperature is a highly nonlinear phenomenon, so to further improve the accuracy of the linear models, a hybrid modeling approach has been proposed. The inclusion of state-of-the-art nonlinear models such as Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) improves the forecasting accuracy of the linear models significantly. Results show that the hybrid model obtained using BDM and ANN is the best fit model.

ANOMALI PASAR PADA RETURN SAHAM LQ 45 DI BURSA EFEK INDONESIA: FENOMENA THE DAY OF WEEK EFFECT, WEEK FOUR EFFECT, ROGALSKY EFFECT, DAN JANUARY EFFECT

2016 ◽  
Vol 12 (1) ◽  
pp. 51
Author(s):  
Reza Widhar Pahlevi
Keyword(s):  
Empirical Research ◽  
Stock Exchange ◽  
Abnormal Returns ◽  
Weak Form ◽  
Strong Form ◽  
January Effect ◽  
Efficient Markets ◽  
Efficient Market ◽  
Market Anomalies ◽  
Day Of The Week

Market anomalies appears on all forms of efficient markets, both weak form, semi-strong and strongform. But plenty of evidence to link the anomaly with semi-strong form efficient market exploited togenerate abnormal returns. Market anomalies that is often discussed is the Day of the Week Effect,January Effect, Week Four Effect and other market anomalies. Empirical research is intended todetermine whether there is the phenomenon of the day of the week effect, week four effect, the effectrogalsky and January effect on LQ 45 stocks in the Indonesia Stock Exchange year period 2014-2015.Based on the analysis of data, shows that there is the phenomenon of the day of week effect on thecompany LQ-45 in Indonesia Stock Exchange 2014-2015 period, there is the phenomenon of weekfour effect on the LQ-45 in Indonesia Stock Exchange 2014-2015 period, there are phenomenonRogalski Effect on the LQ-45 in Indonesia Stock Exchange 2014-2015 period and there is no Januaryeffect phenomenon in the LQ-45 in Indonesia Stock Exchange 2014-2015 period.Keywords: the day of the week effect, week four effect, rogalsky effect and january effect

scholarly journals Implementasi Long Short-Term Memory untuk Identifikasi Berita Hoax Berbahasa Inggris pada Media Sosial

2020 ◽  
Vol 4 (2) ◽  
pp. 179-187
Author(s):  
Jasman Pardede ◽  
Raka Gemi Ibrahim
Keyword(s):  
Logistic Regression ◽  
Short Term Memory ◽  
Support Vector ◽  
Short Term ◽  
Recall Accuracy ◽  
Term Memory ◽  
Support Vector Classifier ◽  
Long Short Term Memory ◽  
F Measure

Hoax atau berita palsu menyebar sangat cepat di media sosial. Berita itu dapat memengaruhi pembaca dan menjadi racun pikiran. Masalah seperti ini harus diselesaikan secara strategis untuk mengidentifikasi berita yang dibaca yang disebarluaskan di media sosial. Beberapa metode yang diusulkan untuk memprediksi tipuan adalah menggunakan Support Vector Classifier, Logistic Regression, dan MultinomialNaiveBayes. Dalam studi ini, para peneliti menerapkan Long Short-Term Memory untuk mengidentifikasi hoax. Kinerja sistem diukur berdasarkan nilai precision, recall, accuracy, dan F-Measure. Berdasarkan hasil eksperimen yang dilakukan pada data tipuan diperoleh nilai rata-rata precision, recall, accuracy, dan F-Measure masing-masing 0,94, 0,96, 0,94, dan 0,95. Berdasarkan hasil eksperimen ditemukan bahwa Long Short-Term Memory yang diusulkan memiliki kinerja yang lebih baik dibandingkan dengan metode sebelumnya.

scholarly journals Predictability of stock market indexes following large drawdowns and drawups

2021 ◽  
Vol 19 (1) ◽  
pp. 1-23
Author(s):  
Vinicius Ratton Brandi
Keyword(s):  
Stock Markets ◽  
Abnormal Returns ◽  
Efficient Market Hypothesis ◽  
Fixed Time ◽  
Uncertain Information ◽  
Short Term ◽  
Efficient Market ◽  
Information Hypothesis ◽  
Novel Approach ◽  
Lower Magnitude

The efficient market hypothesis is one of the most popular subjects in the empirical finance literature. Previous studies of the stock markets, which are mostly based on fixed-time price variations, have inconclusive findings: evidence of short-term predictability varies according to different samples and methodologies. We propose a novel approach and use drawdowns and drawups as triggers, to investigate the existence of short-term abnormal returns in the stock markets. As these measures are not computed within a fixed time horizon, they are flexible enough to capture subordinate, time-dependent processes that could drive market under- or overreaction. Most estimates in our results support the efficient market hypothesis. The underreaction hypothesis receives stronger support than does overreaction, with higher prevalence of return continuations than reversals. Evidence for the uncertain information hypothesis is present in some markets, mainly after lower-magnitude events.

scholarly journals Patient similarity analytics for explainable clinical risk prediction

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hao Sen Andrew Fang ◽  
Ngiap Chuan Tan ◽  
Wei Ying Tan ◽  
Ronald Wihal Oei ◽  
Mong Li Lee ◽  
...  
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Clinical Practice ◽  
Risk Prediction ◽  
Clinical Decision ◽  
Support Vector ◽  
Prototype System ◽  
Care Clinic ◽  
Clinical Risk ◽  
Similarity Model

Abstract Background Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility. Explainability is the extent of which a model’s prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model. Methods The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CRPM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used modified K-nearest neighbour which incorporated expert input, to develop a patient similarity model on this real-world training dataset (n = 7,041) and validated it on a testing dataset (n = 3,018). The results were compared using logistic regression, random forest (RF) and support vector machine (SVM) models from the same dataset. The patient similarity model was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process. Results The patient similarity model (AUROC = 0.718) was comparable to the logistic regression (AUROC = 0.695), RF (AUROC = 0.764) and SVM models (AUROC = 0.766). We packaged the patient similarity model in a prototype web application. A proof of concept demonstrated how the application provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy. Conclusions Patient similarity analytics is a feasible approach to develop an explainable and interpretable CRPM. While the approach is generalizable, it can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice.

scholarly journals Oil Market Efficiency under a Machine Learning Perspective

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 157-168 ◽  
Author(s):  
Athanasia Dimitriadou ◽  
Periklis Gogas ◽  
Theophilos Papadimitriou ◽  
Vasilios Plakandaras
Keyword(s):  
Machine Learning ◽  
Selection Process ◽  
Relevant Literature ◽  
Oil Prices ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Support Vector ◽  
Efficient Market ◽  
Explanatory Variables ◽  
Oil Market

Forecasting commodities and especially oil prices have attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market, attempting to forecast the West Texas Intermediate oil prices under a machine learning framework. In doing so, we compile a dataset of 38 potential explanatory variables that are often used in the relevant literature. Next, through a selection process, we build forecasting models that use past oil prices, refined oil products and exchange rates as independent variables. Our empirical findings suggest that the Support Vector Machines (SVM) model coupled with the non-linear Radial Basis Function kernel outperforms the linear SVM and the traditional logistic regression (LOGIT) models. Moreover, we provide evidence that points to the rejection of even the weak form of efficiency in the oil market.

scholarly journals Patient Similarity Analytics for Explainable Clinical Risk Prediction

2021 ◽  
Author(s):  
Hao Sen Andrew Fang ◽  
Ngiap Chuan Tan ◽  
Wei Ying Tan ◽  
Ronald Wihal Oei ◽  
Mong Li Lee ◽  
...  
Keyword(s):  
Decision Making ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Clinical Practice ◽  
Random Forest ◽  
Risk Prediction ◽  
Clinical Decision ◽  
Support Vector ◽  
Clinical Risk ◽  
Similarity Model

Abstract Background: A Clinical Risk Prediction Model (CRPM) uses patient characteristics to estimate the probability about having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be adopted routinely in clinical practice. The lack of explainability and interpretability has limited its utility. Explainability is the extent of which a model’s prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model.Methods: The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CPRM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used various techniques, including patient similarity analytics, to develop various models on this real-world training dataset (n=7,041) and validated each of them on the same test dataset (n=3,018). The results were compared using logistic regression, random forest and support vector machine models from the same dataset. The CRPM was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process.Results: The patient similarity model (AUROC=0.718) was comparable to the logistic regression (AUROC=0.695), random forest (AUROC=0.764) and support vector machine models (AUROC=0.766). We incorporated the patient similarity model in a prototype web application. A case study demonstrated how the application was provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy.Conclusions: A patient similarity approach is feasible to develop an explainable and interpretable CRPM. It is a general approach which can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice.

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