scholarly journals IMPACT OF INVESTOR SENTIMENTS ON FUTURE TRADING

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Basheer Ahmad ◽  
Keyword(s):  
Stock Return ◽  
Realized Volatility ◽  
Trading Strategies ◽  
Futures Trading ◽  
Monthly Data ◽  
Future Trading ◽  
Investor Sentiments ◽  
Trading Model ◽  
Practical Implications

Purpose: This study bridges the gap between future trading prospects and information required to mold investors’ sentiments so s/he could devise better future trading strategies. Methodology/Sampling: This study takes all companies which trade the futures on the KSE. This study used the monthly data of the futures trading, stock return, stock turnover, high-low ratio and the realized volatility. The data is taken from January 2008 to December 2012 to test investor sentiments impact on future trading. Companies’ data is retrieved from the official website of KSE. The thirty-eight companies’ data is used in this study. Findings: The contrivance of future trading relationship with the investor sentiments is appraised in this study. The main difference between this and the previous discourse is that we construct the futures trading model that employ the investor sentiments. Practical Implications: The verdict of this study holds the important useful implications particularly as consideration of investor sentiments in the presence of the futures trading in Pakistan.

scholarly journals EXAMINING THE NATURE OF RESILIENCE, ESPECIALLY AROUND ISSUES OF RISK AND SECURITY IN RELATION TO CRITICAL NATIONAL INFRASTRUCTURE AND FROM CATASTROPIC EVENTS

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Imran Khan ◽  
Keyword(s):  
Stock Return ◽  
Realized Volatility ◽  
Trading Strategies ◽  
Futures Trading ◽  
Monthly Data ◽  
Future Trading ◽  
Investor Sentiments ◽  
Trading Model ◽  
National Infrastructure ◽  
Practical Implications

Purpose- This study bridges the gap between future trading prospects and information required to mold investors’ sentiments so s/he could devise better future trading strategies. Methodology/Sampling- This study takes all companies which trade the futures on the KSE. This study used the monthly data of the futures trading, stock return, stock turnover, high-low ratio and the realized volatility. The data is taken from January 2008 to December 2012 to test investor sentiments impact on future trading. Companies’ data is retrieved from the official website of KSE. The thirty-eight companies’ data is used in this study. Findings- The contrivance of future trading relationship with the investor sentiments is appraised in this study. The main difference between this and the previous discourse is that we construct the futures trading model that employ the investor sentiments. Practical Implications- The verdict of this study holds the important useful implications particularly as consideration of investor sentiments in the presence of the futures trading in Pakistan.

scholarly journals Causality Relationship between Trading Volume and Stock Return: Evidence from Nepalese Stock Market

2016 ◽  
Vol 9 (1) ◽  
pp. 13-28
Author(s):  
Shivaram Shrestha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Price ◽  
Trading Volume ◽  
Stock Return ◽  
Causal Relation ◽  
Data Sets ◽  
Monthly Data ◽  
Trading Model ◽  
Causality Relationship

This paper aims to empirically examine the causal relation between trading volume and stock returns for Nepalese Stock Market using Garner causality procedure, using monthly data for the period of July 2007 to February 2015. The study analyzed for the investigation of the Granger causality between trading volume and stock price using monthly data sets to ascertain if the causality runs from volume to stock price or from stock price to volume or in both directions. This study detected unidirectional causality from stock returns to trading volume that is indicative of noise trading model of return volume interaction in this market.

CFTC overhauls customer protection requirements

2014 ◽  
Vol 15 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Robert M. Brown
Keyword(s):  
Design Methodology ◽  
Commodity Futures ◽  
Regulatory Regime ◽  
Futures Trading ◽  
Content Type ◽  
Regulatory Response ◽  
Market Participants ◽  
Policies And Procedures ◽  
Practical Implications

Purpose – The purpose of the paper is to summarize the Commodity Futures Trading Commission's (CFTC) recent overhaul of its customer protection rules, which regulate how futures commission merchants (FCMs) and derivatives clearing organizations (DCOs) handle customer funds. Design/methodology/approach – The paper summarizes the most significant aspects of the CFTC's October 30, 2013 customer protection rulemaking, explains FCM and DCO obligations under the new regulatory regime, and sets forth a compliance timeline. Findings – The CFTC's recent overhaul of its customer protection rules impose significant new requirements on FCMs and DCOs in their handling of customer funds. Practical implications – All FCMs and DCOs that handle customer funds should review these new rules and begin putting into place policies and procedures to ensure their compliance as each new requirement comes into effect. Originality/value – The CFTC's overhaul of its customer protection regime is new and significant. FCMs and DCOs need to understand their new obligations under the rules. As these new rules are the CFTC's regulatory response to the events that led to the insolvencies of MF Global and Peregrine Financial Group, these developments also should be of interest to futures and swaps market participants generally.

scholarly journals Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach

2020 ◽  
Vol 34 (02) ◽  
pp. 2128-2135
Author(s):  
Yang Liu ◽  
Qi Liu ◽  
Hongke Zhao ◽  
Zhen Pan ◽  
Chuanren Liu
Keyword(s):  
Reinforcement Learning ◽  
Trading Strategies ◽  
Financial Data ◽  
Imitation Learning ◽  
Market Condition ◽  
Exploration And Exploitation ◽  
Markov Decision ◽  
Trading Model ◽  
Trading Agent ◽  
Partially Observable

In recent years, considerable efforts have been devoted to developing AI techniques for finance research and applications. For instance, AI techniques (e.g., machine learning) can help traders in quantitative trading (QT) by automating two tasks: market condition recognition and trading strategies execution. However, existing methods in QT face challenges such as representing noisy high-frequent financial data and finding the balance between exploration and exploitation of the trading agent with AI techniques. To address the challenges, we propose an adaptive trading model, namely iRDPG, to automatically develop QT strategies by an intelligent trading agent. Our model is enhanced by deep reinforcement learning (DRL) and imitation learning techniques. Specifically, considering the noisy financial data, we formulate the QT process as a Partially Observable Markov Decision Process (POMDP). Also, we introduce imitation learning to leverage classical trading strategies useful to balance between exploration and exploitation. For better simulation, we train our trading agent in the real financial market using minute-frequent data. Experimental results demonstrate that our model can extract robust market features and be adaptive in different markets.

A THRESHOLD MODEL FOR STOCK RETURN VOLATILITY AND TRADING VOLUME

2000 ◽  
Vol 03 (03) ◽  
pp. 467-472 ◽  
Author(s):  
GIULIA IORI
Keyword(s):  
Transaction Costs ◽  
Trading Volume ◽  
Stock Return ◽  
Cross Correlation ◽  
Threshold Model ◽  
Feedback Mechanism ◽  
Return Volatility ◽  
Volatility Clustering ◽  
Stock Return Volatility ◽  
Future Trading

We propose a model with heterogeneous interacting traders which can explain the observed cross-correlation between stock return volatility and trading volume. Transaction costs are introduced which, by responding to price movements, create a feedback mechanism on future trading and generates volatility clustering.

“The lost decade”: an MPT perspective

2015 ◽  
Vol 41 (11) ◽  
pp. 1202-1220 ◽  
Author(s):  
Anthony Loviscek
Keyword(s):  
Design Methodology ◽  
Annual Rate ◽  
Rate Of Return ◽  
Average Risk ◽  
Monthly Data ◽  
Content Type ◽  
Core Set ◽  
Out Of Sample ◽  
Lost Decade ◽  
Practical Implications

Purpose – The purpose of this paper is to test the efficacy of an application of modern portfolio theory (MPT) from 2000 through 2009, a period during which the annual rate of return on the S & P 500 is negative. The financial media have called this period “the lost decade” for investors. Design/methodology/approach – Using monthly data, the author uses a series of annual out-of-sample tests to compare the risk-reward performances of MPT portfolios against those of the S & P 500. Findings – The author finds that the MPT portfolios outperformed the S & P 500. During the “lost decade”. They generated a cumulative return of over 77 percent compared to a cumulative return of −9.1 percent on the S & P 500. Moreover, the MPT portfolio β’s are low, ranging from 0.45 to 1.01, suggesting above-average risk-reward performances. Research limitations/implications – The MPT portfolios are relatively small, and might not be well diversified. That said, they comprise a core set of securities that could help investors achieve a risk-reward performance that exceeds that of the S & P 500. Practical implications – The results suggest that investors should not overlook the potential of MPT, despite its theoretical and practical limitations, to provide above-average returns at below-average risks. Originality/value – This is the first study to show the efficacy of MPT during a period in which it was criticized at having failed investors when they needed it most.

scholarly journals Trading VIX futures under mean reversion with regime switching

2016 ◽  
Vol 03 (03) ◽  
pp. 1650021 ◽  
Author(s):  
Jiao Li
Keyword(s):  
Regime Switching ◽  
Markov Chain Model ◽  
Mean Reversion ◽  
Coupled System ◽  
Numerical Approach ◽  
Trading Strategies ◽  
Chain Model ◽  
Futures Trading ◽  
Vix Futures ◽  
Nicolson Scheme

This paper studies the optimal VIX futures trading problems under a regime-switching model. We consider the VIX as mean reversion dynamics with dependence on the regime that switches among a finite number of states. For the trading strategies, we analyze the timings and sequences of the investor’s market participation, which leads to several corresponding coupled system of variational inequalities. The numerical approach is developed to solve these optimal double stopping problems by using projected-successive-over-relaxation (PSOR) method with Crank–Nicolson scheme. We illustrate the optimal boundaries via numerical examples of two-state Markov chain model. In particular, we examine the impacts of transaction costs and regime-switching timings on the VIX futures trading strategies.

scholarly journals A Cooperative Coevolutionary Stock Trading Model Using Genetic Network Programming-Sarsa

2012 ◽  
Vol 16 (5) ◽  
pp. 581-590 ◽  
Author(s):  
Yang Yang ◽  
◽  
Zhaoping He ◽  
Shingo Mabu ◽  
Kotaro Hirasawa
Keyword(s):  
Stock Market ◽  
Genetic Network ◽  
Trading Strategies ◽  
The Other ◽  
Network Programming ◽  
Cooperative Coevolution ◽  
Stock Trading ◽  
Trading Model ◽  
Genetic Network Programming

This paper presents a cooperative coevolutionary approach for stock trading model using Genetic Network Programming-Sarsa called CCGNP-Sarsa. Although theoretically, a single algorithm with sufficient size could solve any problem, in practice the stock market problem is too large and too complex to construct the appropriate algorithm to solve it. For such problems, cooperative coevolution which simultaneously evolves several species with the sum of their fitness values has been proposed as a successful alternative and was applied to make the stock trading models an integrated one. Such an approach allows different species of the GNP-Sarsa model to evolve in a parallel and cooperative manner, which makes the generated model more robust, generalized and efficient for generating stock trading strategies. CCGNP-Sarsa places as few restrictions as possible to the structure, allowing the model to obtain a wide variety of architecture during the evolution and to be easily used to solve complicated problems. To confirm the effectiveness of the proposed method, the simulations are carried out and compared with other methods like GNP-Sarsa with subroutines, GNP-Sarsa and Buy&Hold method. The results shows that the stock trading models using CCGNP-Sarsa outperforms all the other methods.

scholarly journals Is it possible to predict food retail prices? Evidence from Lithuanian market

2019 ◽  
Author(s):  
Artiom Volkov ◽  
Mangirdas Morkūnas ◽  
Viktorija Skvarciany
Keyword(s):  
Econometric Model ◽  
Food Prices ◽  
Food Retail ◽  
Monthly Data ◽  
Main Determinant ◽  
Budget Planning ◽  
Illicit Market ◽  
Retail Prices ◽  
Retail Food ◽  
Practical Implications

Purpose – the purpose of the article is to develop a model that could be used for estimating the level of the effect of the highlighted determinants on food retail prices. Research methodology – the study is based on the obtained monthly data of food retail prices that covers the period from 2016 I m. to 2018 XII m. (36 observations). Multiple regression modelling is used in order to create a model of food retail prices. Findings – the results provide evidence that the most influential determinants are the price of the alternative products and purchasing power. It also contributes to scholarly thinking, stating, that it is possible to predict the future retail price of a particular product. Research limitations – the limitation of the current study is that the proposed econometric model is sufficient for the Lithuanian market and ought to be modified if used in other countries. Practical implications – the development model allows to predict/forecast the food retail prices which are crucial for households budget planning. Originality/Value – the current study examines the main determinant of retail food prices. It laid a background for future researches, based on examining possibilities to forecast food prices. The research results contribute to classic economic views about market imperfections influence onto supply-demand equilibrium and unproductiveness of consumer illicit market.

scholarly journals Predictive Power of Output Growth, Inflation, and Interest Rate on Stock Return and Volatility: A Comparison

2010 ◽  
Author(s):  
Wai Ching Poon ◽  
Gee Kok Tong
Keyword(s):  
Interest Rate ◽  
Emerging Markets ◽  
Stock Returns ◽  
Stock Return ◽  
Predictive Power ◽  
Output Growth ◽  
Stock Market Volatility ◽  
Monthly Data ◽  
Stock Return Volatility ◽  
Economic Volatility

Using monthly data from seven mature and emerging markets and a battery of GARCH and EGARCH models, the study of Davis and Kutan (2003) on inflation and output on stock returns and volatility is extended by including interest rate to compare the effect between three mature markets (US, Japan, and Singapore) and four emerging markets who experienced a crisis before (Malaysia, India, Korea, and Philippines). It is found that economic volatility, as measured by movement in inflation, output growth, and interest rate, have a weak predictor power for stock market volatility and returns. In line with the evidence reported in Davis and Kutan (2003), the findings suggest that there is no support for the Fisher effect in stock returns among the seven mature and emerging markets.   Keywords: Predictive power; output; inflation; interest rate; stock return volatility.  

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