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Journal articles on the topic 'Time trading'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Djankov, Simeon, Caroline Freund, and Cong S. Pham. "Trading on Time." Review of Economics and Statistics 92, no. 1 (2010): 166–73. http://dx.doi.org/10.1162/rest.2009.11498.

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2

SAYYED, ZAID. "Real Time-Cutting Algorithmic Trading." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35766.

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a. Brief overview of algorithmic trading. Algorithmic trading, also known as algo trading or automated trading, refers to the use of computer algorithms and mathematical models to execute trading orders in financial markets. The primary goal of algorithmic trading is to achieve efficient and optimized execution of trading strategies, leveraging the speed and precision of computer systems. Here is a brief overview of algorithmic trading: 1. **Automation:** Algorithmic trading involves automating the process of buying or selling financial instruments, such as stocks, bonds, currencies, or commod

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Li, Tong, Yuheng Li, Junpeng Gao, Benhua Qian, and Hai Zhao. "A Reputation-Based Pricing Strategy for Distributed Diverse Entity Systems: Enhancing Market Efficiency Through Real-Time Reputation Updates." Sustainability 16, no. 24 (2024): 11216. https://doi.org/10.3390/su162411216.

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Although existing studies address the reduction of default rates by adjusting electricity trading rankings based on reputation values, the mechanisms for penalizing electricity trading defaults remain incomplete. Therefore, this paper proposes a real-time reputation-based pricing method for distributed diverse entity systems to mitigate electricity trading defaults. First, a reputation reward and penalty mechanism evaluates the trading behavior of diverse entities. Next, a ‘price-dominant, reputation-auxiliary’ pricing concept guides the process. Following this, a reputation-driven pricing str

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Lei, Qin, and Xuewu Wang. "Time-Varying Liquidity Trading, Private Information and Insider Trading." European Financial Management 20, no. 2 (2012): 321–51. http://dx.doi.org/10.1111/j.1468-036x.2011.00634.x.

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Asem, Ebenezer, and Aditya Kaul. "Trading time and trading activity: evidence from extensions of the NYSE trading day." European Journal of Finance 14, no. 3 (2008): 225–42. http://dx.doi.org/10.1080/13518470801892236.

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6

Snell, Daniel C. "Marketless Trading in Our Time." Journal of the Economic and Social History of the Orient 34, no. 3 (1991): 129. http://dx.doi.org/10.2307/3632241.

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Jain, Vanita, Dharmender Saini, and Akshit Ahluwalia. "Real-time autonomous trading system." Journal of Statistics and Management Systems 22, no. 2 (2019): 403–13. http://dx.doi.org/10.1080/09720510.2019.1582881.

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8

Snell, Daniel C. "Marketless Trading in Our Time." Journal of the Economic and Social History of the Orient 34, no. 2 (1991): 129–41. http://dx.doi.org/10.1163/156852091x00085.

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9

Back, Kerry. "Insider Trading in Continuous Time." Review of Financial Studies 5, no. 3 (1992): 387–409. http://dx.doi.org/10.1093/rfs/5.3.387.

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10

Valiente, Gabriel. "Trading uninitialized space for time." Information Processing Letters 92, no. 1 (2004): 9–13. http://dx.doi.org/10.1016/j.ipl.2004.06.002.

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11

Markowicz, Iwona. "Modeling the Survival Time of Trading Companies in the Zachodniopomorskie Voivodship." Acta Universitatis Lodziensis. Folia Oeconomica 4, no. 337 (2018): 85–97. http://dx.doi.org/10.18778/0208-6018.337.06.

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The aim of this study was to construct models of trading companies’ lifespan, in individual districts of Zachodniopomorskie Voivodship. The author verified whether the impact of the survival time of trading companies on the survival function in general is the same in individual districts. This may inform potential entrepreneurs’ decisions on whether to set up a trading or other company. The Kaplan‑Meier estimator was calculated and a tests verifying similarities of functions of trading companies within the districts was used. Districts were then divided into groups, according to trading compan

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12

Al-Sulaiman, Talal. "Review of Recent Research Directions and Practical Implementation of Low-Frequency Algorithmic Trading." American Journal of Financial Technology and Innovation 2, no. 1 (2024): 1–14. http://dx.doi.org/10.54536/ajfti.v2i1.2354.

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Financial trading has undergone substantial technological evolution, with automation taking center stage, leading to approximately 80% of US market trades being executed by computer systems, predominantly by large financial institutions. The rise of algorithmic trading, poised to engage smaller entities, international markets, and individual traders, drives this article’s exploration of research in this field. Providing a comprehensive overview, it outlines the evolution of trading practices and defines algorithmic trading as a computer-powered tool aiding investment decisions. The article det

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13

Pushpam, Devanand Nagdeve, and Pareek Peeyush. "A Centralized Platform: For Expert-Generated Trading Signals in Real-Time." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 34–38. https://doi.org/10.5281/zenodo.7922750.

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Binary options trading is a type of financial trading where the trader speculates on the direction of the price of an underlying asset within on specified period. Unlike traditional financial trading, binary options trading provides a fixed payout if the trader's prediction is correct, regardless of how much the price moves in the predicted direction. This makes binary options trading a popular choice among traders who want to know their potential payout in advance and limit their risk. However, binary options trading is complex and unpredictable, making it challenging for individual trade

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14

Habib, Reza. "Optimal Time for Closing a Trading Position i." Athens Journal of Business & Economics 10, no. 4 (2024): 309–18. http://dx.doi.org/10.30958/ajbe.10-4-4.

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In this paper, trading rules (strategies) on a specified financial asset at some future time are interpreted as contingent claims (financial derivatives). Therefore, their fair values are computable using the binomial tree technique. However, traders pay the price of financial asset at the current time to enter to trading. Clearly, it is a loss for traders. In this paper, first, hedging strategies are proposed. Then, using three procedures the optimal time for closing the trading position are derived. Mentioned procedures are based on optimal stopping time and stochastic dynamic programming, s

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15

Dou, Xun, Li Song, Shengnan Zhang, Lulu Ding, Ping Shao, and Xiaojun Cao. "Multi-Time Scale Trading Simulation of Source Grid Load Storage Based on Continuous Trading Mechanism for China." Sensors 22, no. 6 (2022): 2363. http://dx.doi.org/10.3390/s22062363.

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The proportion of new energy in power systems is increasing yearly. How to deal with the adverse impact of new energy output uncertainty on its participation in trading from the mechanism level is an urgent problem in China that must be solved. A source grid load storage (SGLS) continuous trading mechanism and a multi-time scale trading simulation method are proposed which meet the needs of Chinese new energy consumption and satisfies the trading needs of Chinese power market players. Firstly, the connection mechanism of mid-long term, day-ahead, and intra-day SGLS interactive trading is estab

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16

Corcoran, Elizabeth, and Paul Wallich. "Trading Leisure Time for More Goods?" Scientific American 265, no. 3 (1991): 176. http://dx.doi.org/10.1038/scientificamerican0991-176.

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17

Yang, Luyi, Laurens Debo, and Varun Gupta. "Trading Time in a Congested Environment." Management Science 63, no. 7 (2017): 2377–95. http://dx.doi.org/10.1287/mnsc.2016.2436.

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18

Decamps, Marc, and Ann De Schepper. "Edgeworth expansions of stochastic trading time." Physica A: Statistical Mechanics and its Applications 389, no. 16 (2010): 3179–92. http://dx.doi.org/10.1016/j.physa.2010.04.014.

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19

Johnson, Eric, and Russell Heinen. "Carbon trading: time for industry involvement." Environment International 30, no. 2 (2004): 279–88. http://dx.doi.org/10.1016/j.envint.2003.09.001.

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20

Morgan, Cynthia, and Ann Wolverton. "Water quality trading in the United States: trading programs and one-time offset agreements." Water Policy 10, no. 1 (2007): 73–93. http://dx.doi.org/10.2166/wp.2007.028.

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This paper provides a systematic overview of water quality trading programs and one-time offset agreements in the USA. The primary source of information for this overview is a detailed database, collected and compiled by a team of researchers at Dartmouth College. Details discussed include: sources of the pollutant, types of pollutants traded, legal liability, main regulatory drivers, market structure, trading ratios, transaction and administrative costs and difficulties encountered in trading. We find that trading has often been explored as a way to meet more stringent discharge limits or wat

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21

Xiao, Kai, and Yonghui Zhou. "Linear Bayesian equilibrium in insider trading with a random time under partial observations." AIMS Mathematics 6, no. 12 (2021): 13347–57. http://dx.doi.org/10.3934/math.2021772.

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<abstract><p>In this paper, the insider trading model of Xiao and Zhou (<italic>Acta Mathematicae Applicatae, 2021</italic>) is further studied, in which market makers receive partial information about a static risky asset and an insider stops trading at a random time. With the help of dynamic programming principle, we obtain a unique linear Bayesian equilibrium consisting of insider's trading intensity and market liquidity parameter, instead of none Bayesian equilibrium as before. It shows that (i) as time goes by, both trading intensity and market depth increase expon

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22

Tian, Yisong. "Optimal Bond Trading with Tax Clienteles: A Discrete-Time Dynamic Trading Model." Financial Review 31, no. 2 (1996): 313–41. http://dx.doi.org/10.1111/j.1540-6288.1996.tb00875.x.

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23

Kim, Sang, Hee Lee, Han Ko, Seung Jeong, Hyun Byun, and Kyong Oh. "Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm." Sustainability 10, no. 12 (2018): 4641. http://dx.doi.org/10.3390/su10124641.

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The futures market plays a significant role in hedging and speculating by investors. Although various models and instruments are developed for real-time trading, it is difficult to realize profit by processing and trading a vast amount of real-time data. This study proposes a real-time index futures trading strategy that uses the KOSPI 200 index futures time series data. We construct a pattern matching trading system (PMTS) based on a dynamic time warping algorithm that recognizes patterns of market data movement in the morning and determines the afternoon’s clearing strategy. We adopt 13 and

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24

Jaiswal, Gulab Chand, and Dharen Kumar Pandey. "A Brief Introduction to the Commodity Trading in India." Anushilana XXXVI (April 1, 2011): 123–28. https://doi.org/10.5281/zenodo.4698499.

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After a long time India has experienced the commodity trading at a time when its people has forgot the trading process with a very low interest gaining a good pace after ten years of its introduction. It is necessary to thus introduce the new generation with the modern commodity trading mechanism. This paper in short aims at introducing three points: what commodity trading means; where it takes place i.e. Commodity exchanges, and, how is it regulated in the Indian economy.

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25

CHOI, SAMUEL P. M., and JIMING LIU. "MARKOV DECISION APPROACH FOR TIME-CONSTRAINED TRADING IN ELECTRONIC MARKETPLACE." International Journal of Information Technology & Decision Making 01, no. 03 (2002): 511–23. http://dx.doi.org/10.1142/s0219622002000324.

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The recent advent in Internet technology has made electronic trading increasingly popular. Nevertheless, existing electronic trading systems still heavily rely on human decision making. In order to facilitate the new trading environment, one of the main trends is to employ autonomous agents as representatives of human buyers and sellers. In particular, given a user requirement and an imposed deadline, an autonomous agent has to search for possible deals from an electronic marketplace on behalf of its owner. One problem such an agent must face is: given a collection of offers and the remaining

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26

Karn, Arodh Lal, YE Qiang, Rakshha Kumari Karna, and Xiaolin Wang. "News Sentiment Incorporation in Real-Time Trading." Journal of Global Information Management 26, no. 4 (2018): 18–35. http://dx.doi.org/10.4018/jgim.2018100102.

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This article describes how machines are the new breed of traders as news sentiment arrivals drive the stock price change. Strategies are the technical approach to search for profit from event-based speculations. This paper revisits these topics in a novel way and first uncovers distinctive characteristics of high frequency trading in Helsinki stock exchange insinuating the impression on positive recovers of event trading. Here is a better prediction by the incorporation of news on returns that proposed event trading strategy has significant effects on Finnish stock. This article contributes to

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27

Liu, Tiantian, Ning Qiu, and Wentao Gu. "A Stock Trading Strategy Based on Time-Varying Quantile Theory." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 3 (2015): 417–22. http://dx.doi.org/10.20965/jaciii.2015.p0417.

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Many of the trading strategies viewed as highly important by to financial market investors, we developed based on fundamental and technical analysis. We propose a stock trading strategy based on time-varying quantile analysis and apply it to the stock market in the People’s Republic of China. Comparing results for both the buy-and-hold strategy and a popular NARX-based neural network trading strategy showed that our strategy performed well.

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28

Liu, Zhongming, Hang Luo, Peng Chen, Qibin Xia, Zhihao Gan, and Wenyu Shan. "An efficient isomorphic CNN-based prediction and decision framework for financial time series." Intelligent Data Analysis 26, no. 4 (2022): 893–909. http://dx.doi.org/10.3233/ida-216142.

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Financial time series prediction and trading decision-making are priorities of computational intelligence for researchers in academia and the finance industry due to their broad application areas and substantial impact. However, these methods remain challenging because they retain various complex statistical properties, and the mechanism behind the processes is unknown to a large extent. A significant number of machine learning-based methods are proposed and demonstrate impressive results, especially deep learning-based models. Nevertheless, due to the high complexity of massive, nonlinear, an

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29

Ryu, Doojin. "INFORMATION CONTENT OF INTER-TRANSACTION TIME: A STRUCTURAL APPROACH." Journal of Business Economics and Management 16, no. 4 (2015): 697–711. http://dx.doi.org/10.3846/16111699.2013.804873.

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This study examines the information role of inter-transaction time by employing a structural market microstructure model. By analyzing the intraday data of the KOSPI200 futures market, we find that the inter-transaction time (i.e., time between two consecu- tive trades) reveals significant information, and that fast trading is indicative of informed trading. This result remains robust when the effect of trade size is incorporated into the model. Our regression analysis indicates that the information role of inter-transaction time becomes more important when informed trading is less concentrate

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30

Xiao, Kai. "Risk-seeking insider trading with partial observation in continuous time." AIMS Mathematics 8, no. 11 (2023): 28143–52. http://dx.doi.org/10.3934/math.20231440.

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<abstract><p>In this paper, a continuous-time insider trading model is investigated in which an insider is risk-seeking and market makers may receive partial information on the value of a risky asset. With the help of filtering theory and dynamic programming principle, the uniqueness and existence of linear equilibrium is established. It shows that (ⅰ) as time goes by, the residual information decreases, but both the trading intensity and the market liquidity increases, and (ⅱ) with the partial observation accuracy decreasing, both the market liquidity and the residual information

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31

Lusk, Edward J. "Time Series Forecasting in Stock Trading Markets." International Journal of Research in Business and Social Science (2147-4478) 8, no. 4 (2019): 01–16. http://dx.doi.org/10.20525/ijrbs.v8i4.283.

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Univariate Time Series Models [TSM] use only a Panel of historical data to produce forecasts. The tacit belief in using TSM is that the past information portends the future of the longitudinal data-stream. This is likely in certain cases such as strictly Ergodic Panel segments of sufficient size in the overall Panel. A question of interest is: Is the success of TSM in these contexts generalizable? The test of this question used a Litmus-Test design to examine the performance profile of TSM for a longitudinal time series the last point of which is a Turning Point [TP]. Specifically, the inferen

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32

Bebbington, Peter A., and Reimer Kühn. "Optimal trading strategies—a time series approach." Journal of Statistical Mechanics: Theory and Experiment 2016, no. 5 (2016): 053209. http://dx.doi.org/10.1088/1742-5468/2016/05/053209.

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Chakrabarti, Gagari, and Chitrakalpa Sen. "Time series momentum trading in green stocks." Studies in Economics and Finance 37, no. 2 (2020): 361–89. http://dx.doi.org/10.1108/sef-07-2019-0269.

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Purpose The purpose of this study is to explore the inherent instability, if any, in the context of investment in stocks of environment friendly companies (or the “green” stocks) across the globe using the time series momentum (TSM) trading strategies. Design/methodology/approach Using the monthly data for the Green Indexes from the USA, the Europe and the Asia-Pacific region over 2003-2019, the authors construct TSM trading strategies to examine the efficacy of regional Green Indexes as well as two diversified global green portfolios to offer abnormal return to attract investors, particularly

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34

Lei, Qin, and Guojun Wu. "Time-varying informed and uninformed trading activities." Journal of Financial Markets 8, no. 2 (2005): 153–81. http://dx.doi.org/10.1016/j.finmar.2004.09.002.

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35

Bjørnstad, Ottar N. "Trading space for time in population dynamics." Trends in Ecology & Evolution 16, no. 3 (2001): 124. http://dx.doi.org/10.1016/s0169-5347(00)02095-4.

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36

Li, Thomas Nanfeng, and Agnès Tourin. "Optimal pairs trading with time-varying volatility." International Journal of Financial Engineering 03, no. 03 (2016): 1650023. http://dx.doi.org/10.1142/s2424786316500237.

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In this paper, we propose a pairs trading model that incorporates a time-varying volatility of the constant elasticity of variance type. Our approach is based on stochastic control techniques; given a fixed time horizon and a portfolio of two cointegrated assets, we define the trading strategies as the portfolio weights maximizing the expected power utility from terminal wealth. We compute the optimal pairs strategies by using a finite difference method. Finally, we illustrate our results by conducting tests on historical market data at daily frequency. The parameters are estimated by the gene

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37

Choi, Jin Hyuk, Heeyoung Kwon, and Kasper Larsen. "Trading Constraints in Continuous-Time Kyle Models." SIAM Journal on Control and Optimization 61, no. 3 (2023): 1494–512. http://dx.doi.org/10.1137/21m1446617.

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38

Çetin, Umut. "Insider trading with penalties in continuous time." Journal of Economic Theory 228 (September 2025): 106061. https://doi.org/10.1016/j.jet.2025.106061.

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39

Guanghe, Cao, Shuaiqi Zheng, Yibang Liu, and Maoxi Li. "Real-time Anomaly Detection in Dark Pool Trading Using Enhanced Transformer Networks." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 3, no. 4 (2024): 320–29. https://doi.org/10.60087/jklst.v3.n4.p320.

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This paper uses an enhanced transformer network architecture to present a novel approach to real-time anomaly detection in dark pool trading environments. Dark pools facilitate anonymous large-volume trades and require sophisticated surveillance mechanisms to maintain market integrity. We propose a specialized transformer-based framework integrating advanced attention mechanisms with optimized processing pipelines for efficient anomaly detection. The system incorporates modified self-attention patterns and specialized feature engineering techniques for high-frequency trading data. Our implemen

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40

Ye, Shuohong, Mingyu Zhang, and Jiabeizi Yu. "Research on the application of time series ARIMA model in trade strategy." BCP Business & Management 26 (September 19, 2022): 215–22. http://dx.doi.org/10.54691/bcpbm.v26i.1929.

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Market trading has always been a popular choice for bold investors, but market trading doesn't rely on pure luck. More need for daily experience and data. In this article, we will model the random forest algorithm for a specific situation and find the best strategy. Then it is proved that the model can provide the best trading strategy, which can be understood as proving the fit of the model established above. Using the real value of bit coin and gold prices in 5 years, establish a time series ARIMA model, analyze the smoothness and pure randomness of the series.Then get the ARIMA model fittin

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41

Mahoney, Paul. "Equity Market Structure Regulation: Time to Start Over." Michigan Business & Entrepreneurial Law Review, no. 10.1 (2021): 1. http://dx.doi.org/10.36639/mbelr.10.1.equity.

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Over the past half-century, the U.S. Securities and Exchange Commission (SEC)’s regulations have become key determinants of the way in which stocks trade and the fees that exchanges charge for their services. The current equity market structure rules are contained primarily in the SEC’s Regulation NMS. The theory behind Regulation NMS is that a system of dispersed markets operating pursuant to SEC-mandated information and order routing links will provide the benefits of consolidation and competition simultaneously. This article argues that Regulation NMS has failed in that quest. It has produc

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42

Karthickram Vailraj. "Innovations in real-time trade execution: database speed, latency and resilience." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 673–82. https://doi.org/10.30574/wjaets.2025.15.3.0953.

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This comprehensive article examines the technological foundations that enable real-time trade execution in modern financial markets, where performance is measured in microseconds and nanoseconds. The article delves into the sophisticated database architectures that support high-frequency trading environments, highlighting how memory-first designs, zero-copy data streaming, and concurrent write optimization techniques have revolutionized execution speed and market structure. It analyzes how these database innovations directly impact profitability through reduced latency and increased throughput

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Oksanen, A., E. Mantere, I. Vuorinen, and I. Savolainen. "Gambling and online trading: emerging risks of real-time stock and cryptocurrency trading platforms." Public Health 205 (April 2022): 72–78. http://dx.doi.org/10.1016/j.puhe.2022.01.027.

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Alnahedh, Saad A., and Abdullah M. Al-Awadhi. "Individual Traders, Informed Trading, and Stock Market Liquidity." Arab Journal of Administrative Sciences 31, no. 2 (2024): 389–425. https://doi.org/10.34120/ajas.v31i2.1241.

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Purpose: This study examines the relationship between individual trading intensity and stock liquidity, assessing whether individual traders are informed or merely noise traders. We aim to understand individual trading's impact on liquidity in an emerging market setting without market makers.Study design/methodology/approach: We use a comprehensive dataset from intraday individual trading activity across all listed companies in Boursa Kuwait, employing regression models with industry and time fixed effects, different liquidity and probability of informed trading measures, and a Heckman two-sta

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Devan, Munivel, Kumaran Thirunavukkarasu, and Lavanya Shanmugam. "Algorithmic Trading Strategies: Real-Time Data Analytics with Machine Learning." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 3 (2023): 522–46. http://dx.doi.org/10.60087/jklst.vol2.n3.p546.

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Machine learning algorithms have emerged as potent tools for risk control in algorithmic trading, empowering traders to scrutinize vast volumes of market data, discern patterns, and make well-informed trading decisions. In the contemporary, swiftly evolving, and data-centric financial markets, effective risk management is imperative to navigate market uncertainties and optimize trading performance. Traditional risk control methodologies often falter in grasping complex market dynamics and adapting to swiftly changing conditions, thus propelling the adoption of machine learning algorithms. Thes

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Luu, Quoc, Son Nguyen, and Uyen Pham. "Time series prediction: A combination of Long Short-Term Memory and structural time series models." Science & Technology Development Journal - Economics - Law and Management 4, no. 1 (2020): 500–515. http://dx.doi.org/10.32508/stdjelm.v4i1.593.

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Stock market is an important capital mobilization channel for economy. However, the market has potential loss due to fluctuations of stock prices to reflect uncertain events such as political news, supply and demand of daily trading volume. There are many approaches to reduce risk such as portfolio construction and optimization, hedging strategies. Hence, it is critical to leverage time series prediction techniques to achieve higher performance in stock market. Recently, Vietnam stock markets have gained more and more attention as their performance and capitalization improvement. In this work,

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Kenny, Bridget. "Trading Time: Retail working time and precarious labour in South Africa, 1960s–1980s." Journal of Labor and Society 24, no. 1 (2021): 163–86. http://dx.doi.org/10.1163/24714607-20212006.

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Abstract This is a historical piece that traces debates and struggles over store trading times (which determined working time) from the 1960s to the 1980s in Johannesburg, South Africa, to explore the connection of working time debates to the precarianisation of retail labour in South Africa over the ensuing decades. Debates about trading time and working time, and how unions engaged therein, were fundamentally linked with changes to the labour market of service workers over this period. This paper explores how the emergence of precarious labour in sectors like retail can be explained as conju

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Sun, Myung Hwang, and Seok Bong Cho. "The Trading Methodology Using Employing Gibbs Effect at the Market Opening Time." Applied Mechanics and Materials 651-653 (September 2014): 1674–76. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1674.

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We proposed several trading algorithms, which consist of MACD with the Gibbs Effect can measure future price. The proposed algorithms are applied successfully to the market opening time. In addition, we have implemented some useful real-time trading systems through C++ programming, which provide strong availability to combat the future market. The proposed transaction method using the Gibbs effect and MACD has been confirmed to be excellent in the trading success probability and expected returns through simulations. Furthermore, the revenue is expected to be redoubled if this method can be app

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Ryu, Jaepil, and Hyun Joon Shin. "Investment Strategies for KOSPI200 Index Futures Using Negative Correlation of Time-Series." Journal of Derivatives and Quantitative Studies 22, no. 4 (2014): 723–46. http://dx.doi.org/10.1108/jdqs-04-2014-b0006.

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This paper presents 6 time-series that have negative correlation with KOSPI200 Index and a quantitative trading methodology based on stochastic control chart using these time-series. The proposed quantitative trading framework detects trade (long or short) timing by monitoring whether a time-series touches 4 trigger lines, which play a role as control limits in control chart. In other words, a time-series upwardly touches one of trigger line, then the framework take a short position on KOSPI200 Index Futures, while in case of downward touch, it takes a long position. The 6 time-series are deri

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Jha, Saumitra. "Trading for peace*." Economic Policy 33, no. 95 (2018): 485–526. http://dx.doi.org/10.1093/epolic/eiy009.

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Abstract:

AbstractI examine the conditions under which trade can support peaceful coexistence and prosperity when particular social and ethnic groups are cheap targets of violence. A simple theoretical framework reveals that for a broad set of cases, while inter-group competition generates incentives for violence, the presence of non-replicable, non-expropriable inter-group complementarities becomes necessary to sustain peaceful coexistence over long time horizons. In addition to complementarity, two further conditions are important for deterring violence over time. When relatively mobile groups (e.g. i

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