• Title/Summary/Keyword: Value Traded Stocks

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Momentum Strategies and Stock Returns: A Case of Saudi Stock Market

  • KHAN, Muhammad Asif;REHMAN, Ramiz Ur;AHMAD, Muhammad Ishfaq;HARTHI, Majed Al
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.7
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    • pp.365-373
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    • 2021
  • This paper investigates the presence of momentum profits in the Saudi stock market. The study applied a quantitative method by utilizing monthly closing prices of 194 listed firms on Tadawal (Saudi Stock Market). The data from January 2010 to February 2019 is taken from the Tadawal market database for analysis. The sample is further divided into two equal sub-samples based on the structural changes that occurred in the Saudi stock market. Moreover, the high- and low-value traded portfolios are also constructed to examine the presence of momentum profits. Sixteen investment strategies are formed for each sample. The results show a very strong presence of momentum profits in the Saudi stock market for the full sample as well as for the sub-samples. The momentum profits are observed for a longer investment horizon. The results confirm that the short or medium-term formation of portfolios produces negative momentum returns for high-value traded stocks. The low-value traded stocks portfolios give similar results to the full sample results in terms of momentum profits. The results suggest that an investor should keep an eye on the past performance of desired stocks for at least three-nine months in which they are willing to invest.

Group-Performance Based Pay of Publicly Traded Companies and Its Association with Value Added Productivity per Employee

  • Yang, Donghoon
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.7
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    • pp.85-90
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    • 2015
  • This study surveyed 152 publicly traded companies to investigate group-performance based pay practices and its impacts on labor productivity. Compared a benchmark survey from Department of Labor, those companies show higher introduction rates, especially in small-to-medium sized companies. They also tend to pay profit-sharing bonus more in the form of company stocks and differentiate individual bonuses more by department performance than individual performance. The impact of group-performance based pay on labor productivity is positive and statistically significant. Economic value added per person in those companies adopting group-performance based pay tends to be higher and increases with the coverage of employees under the pay plan. It also reveals that the years after the play adoption are negatively associated with labor productivity.

Convergent Momentum Strategy in the Korean Stock Market (한국 주식시장에서의 융합적 모멘텀 투자전략)

  • Koh, Seunghee
    • Journal of the Korea Convergence Society
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    • v.6 no.4
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    • pp.127-132
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    • 2015
  • This study attempts to empirically investigate if relative momentum strategy is effective in the Korean stock market. The sample of the study is comprised of companies which are traded in both Kospi and Kosdaq stock markets in Korea for the period between 2001~2014. The study observes that the momentum strategy buying past winner stocks and selling past loser stocks is negatively correlated with the value strategy buying value stocks with high book to market ratio and selling glamour stocks with low book to market ratio. And each strategy is alternatively effective from period to period. The study demonstrates that the momentum strategy is effective when both strategies which are negatively correlated are treated as one system by estimating Fama and French's[1] 3 factor regression model.

Cryptocurrency Auto-trading Program Development Using Prophet Algorithm (Prophet 알고리즘을 활용한 가상화폐의 자동 매매 프로그램 개발)

  • Hyun-Sun Kim;Jae Joon Ahn
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.46 no.1
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    • pp.105-111
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    • 2023
  • Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

How does the Stock Market Reacts to Information Security Investment of Firms in Korea : An Exploratory Study (기업의 정보보안 투자에 시장이 어떻게 반응하는지에 대한 탐색적 연구)

  • Park, Jaeyoung;Jung, Woojin;Kim, Beomsoo
    • Journal of Information Technology Services
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    • v.17 no.1
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    • pp.33-45
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    • 2018
  • Recently, many South Korean firms have suffered financial losses and damaged corporate images from the data breaches. Accordingly, a firm should manage their IT assets securely through an information security investment. However, the difficulty of measuring the return on an information security investment is one of the critical obstacles for firms in making such investment decisions. There have been a number of studies on the effect of IT investment so far, but there are few researches on information security investment. In this paper, based on a sample of 76 investment announcements of firms whose stocks are publicly traded in the South Korea's stock market between 2001 and 2017, we examines the market reaction to information security investment by using event study methodology. The results of the main effects indicate that self-developed is significantly related to cumulative average abnormal returns (CAARs), while no significant effect was observed for discloser, investment characteristics and firm characteristics. In addition, we find that the market reacts more favorably to the news announced by the subject of investment than the vendor, in case of investments with commercial exploitation. One of main contributions in our study is that it has revealed the factors affecting the market reaction to announcement of information security investment. It is also expected that, in practice, corporate executives will be able to help make an information security investment decision.

Olympic Advertisers Win Gold, Experience Stock Price Gains During and After the Games (오운선수작위엄고대언인영득금패(奥运选手作为广告代言人赢得金牌), 비새중화비새후적고표개격상양(比赛中和比赛后的股票价格上扬))

  • Tomovick, Chuck;Yelkur, Rama
    • Journal of Global Scholars of Marketing Science
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    • v.20 no.1
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    • pp.80-88
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    • 2010
  • There has been considerable research examining the relationship between stockholders equity and various marketing strategies. These include studies linking stock price performance to advertising, customer service metrics, new product introductions, research and development, celebrity endorsers, brand perception, brand extensions, brand evaluation, company name changes, and sports sponsorships. Another facet of marketing investments which has received heightened scrutiny for its purported influence on stockholder equity is television advertisement embedded within specific sporting events such as the Super Bowl. Research indicates that firms which advertise in Super Bowls experience stock price gains. Given this reported relationship between advertising investment and increased shareholder value, for both general and special events, it is surprising that relatively little research attention has been paid to investigating the relationship between advertising in the Olympic Games and its subsequent impact on stockholder equity. While attention has been directed at examining the effectiveness of sponsoring the Olympic Games, much less focus has been placed on the financial soundness of advertising during the telecasts of these Games. Notable exceptions to this include Peters (2008), Pfanner (2008), Saini (2008), and Keller Fay Group (2009). This paper presents a study of Olympic advertisers who ran TV ads on NBC in the American telecasts of the 2000, 2004, and 2008 Summer Olympic Games. Five hypothesis were tested: H1: The stock prices of firms which advertised on American telecasts of the 2008, 2004 and 2000 Olympics (referred to as O-Stocks), will outperform the S&P 500 during this same period of time (i.e., the Monday before the Games through to the Friday after the Games). H2: O-Stocks will outperform the S&P 500 during the medium term, that is, for the period of the Monday before the Games through to the end of each Olympic calendar year (December 31st of 2000, 2004, and 2008 respectively). H3: O-Stocks will outperform the S&P 500 in the longer term, that is, for the period of the Monday before the Games through to the midpoint of the following years (June 30th of 2001, 2005, and 2009 respectively). H4: There will be no difference in the performance of these O-Stocks vs. the S&P 500 in the Non-Olympic time control periods (i.e. three months earlier for each of the Olympic years). H5: The annual revenue of firms which advertised on American telecasts of the 2008, 2004 and 2000 Olympics will be higher for those years than the revenue for those same firms in the years preceding those three Olympics respectively. In this study, we recorded stock prices of those companies that advertised during the Olympics for the last three Summer Olympic Games (i.e. Beijing in 2008, Athens in 2004, and Sydney in 2000). We identified these advertisers using Google searches as well as with the help of the television network (i.e., NBC) that hosted the Games. NBC held the American broadcast rights to all three Olympic Games studied. We used Internet sources to verify the parent companies of the brands that were advertised each year. Stock prices of these parent companies were found using Yahoo! Finance. Only companies that were publicly held and traded were used in the study. We identified changes in Olympic advertisers' stock prices over the four-week period that included the Monday before through the Friday after the Games. In total, there were 117 advertisers of the Games on telecasts which were broadcast in the U.S. for 2008, 2004, and 2000 Olympics. Figure 1 provides a breakdown of those advertisers, by industry sector. Results indicate the stock of the firms that advertised (O-Stocks) out-performed the S&P 500 during the period of interest and under-performed the S&P 500 during the earlier control periods. These same O-Stocks also outperformed the S&P 500 from the start of these Games through to the end of each Olympic year, and for six months beyond that. Price pressure linkage, signaling theory, high involvement viewers, and corporate activation strategies are believed to contribute to these positive results. Implications for advertisers and researchers are discussed, as are study limitations and future research directions.

Analysis of Trading Performance on Intelligent Trading System for Directional Trading (방향성매매를 위한 지능형 매매시스템의 투자성과분석)

  • Choi, Heung-Sik;Kim, Sun-Woong;Park, Sung-Cheol
    • Journal of Intelligence and Information Systems
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    • v.17 no.3
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    • pp.187-201
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    • 2011
  • KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.