Selling Stocks Research Articles

Cheque kiting? I have an idea!!

PurposeThe paper aims to explore the present cheque clearance system in the USA and its possible misuses in the practical sense. Cheque kiting has been a way of creating fictitious cash balances on the balance sheet, which is a fraud. This practice can be prevented by a different accounting treatment.Design/methodology/approachThe paper compares the cheque clearance system in the USA with the author’s own experiences from Turkey. It purports the riskiness of the present cheque clearance system on the financial statements and suggests a practical accounting application to prevent possible financial statement misrepresentations.FindingsThe paper explores that if the present accounting treatment is changed both for the banks and the businesses, the possibility of cheque kiting and misrepresentation of cash balances on the balance sheet will end. Basically, cash is the amount that can be used by the customer, the rest is not cash, it is “cheques in collection” and it should be treated as an account receivable until it is collected.Practical implicationsFinancial statements are vital for the business world. Based on these statements, banks lend money, governments collect taxes and people buy and sell stocks. They need to be presented fairly. The present accounting application on cheque clearance is not transparent enough in the USA, and this might lead to misrepresentation of financial statements. The paper suggests a practical solution to this problem. By changing the accounting treatment, the companies will only show cash in their cash accounts and not the cheques in the collection process.Social implicationsFair treatment is the motto for any situation we face in our daily lives. One may be a poor or rich person but the treatment should be fair unless he/she is a fraudster. A rich person’s cheque is deposited in his/her bank account the next business day, and a poor person’s cheque might take days to be credited. This is not a fair treatment. The paper suggests that there must be a one-way accounting treatment, regardless of the depositor’s financial situation.Originality/valueThis paper has been prepared based on the author’s past business experience in Turkey and his study of the US cheque clearance system, comparing the two. It reflects the real-world examples of cheque kiting and its negative consequences in the USA and proposes a solution.

The Art of Losing in Investing: Harvesting Tax Losses for a Positive Impact

Paying taxes is universally disliked. Tax-loss harvesting strategies help offset the capital gains tax investors incur. Tax-loss harvesting involves selling stocks that have returned negative over a recent time period. These recognized losses can offset investment gains and therefore lower the investor’s capital gains tax bill. The authors argue that tax-loss harvesting strategies are smart beta momentum strategies in disguise. They also argue that just as momentum strategies are managed year-round, tax-loss harvesting strategies should be too, not just in December.

Stock Market Prediction Using Time Series Analysis

Stock market is a market that enables seamless exchange of buying and selling of company stocks. Every Stock Exchange has their own Stock Index value. Index is the average value that is calculated by combining several stocks. This helps in representing the entire stock market and predicting the market’s movement over time. The Equity market can have a profound impact on people and the country’s economy as a whole. Therefore, predicting the stock trends in an effective manner can minimize the risk of investing and maximize profit. In our paper, we are using the Time Series Forecasting methodology for predicting and visualizing the predictions. Our focus for prediction will be based on the technical analysis using historic data and ARIMA Model. Autoregressive Integrated Moving Average (ARIMA) model has been used extensively in the field of finance and economics as it is known to be robust, efficient and has a strong potential for short-term share market prediction.

A league championship algorithm equipped with network structure and backward Q-learning for extracting stock trading rules

Buying and selling stocks are the main activities of investment in the financial markets. During several years, financial experts have designed various indices and indicators to develop intelligent methods to decide whether buy or sell or no trade a specific stock. In this paper, the league championship algorithm (LCA) is adapted and equipped with a network structure (which provides the implicit memory function, compact structure and gives the ability of reusing nodes) for stock trading rule extraction process. The proposed algorithm is able to extract and save various stock trading rules for various kinds of stock market conditions. Each individual in LCA makes role as a sport team which could learn from weaknesses and strengths of others to enhance its performance. The network structure used for each individual is equipped with reinforcement learning to strength the searching power and extracting better rules. The backward Q-learning (BQL), which is a combination of Sarsa learning and Q-learning algorithms, is also hybridized with LCA to increase the learning speed to extract better rules. A trend clustering technique is also developed to determine uptrends and downtrends and to remedy delay in the trend direction reflection along with moving average of stock prices. Finally, to use the trading rules extracted and saved in the rule pools, a new approach through average matching degree is introduced which could create trinary signals of buying, selling, and no trade. The effectiveness of the proposed approach is tested on a set of 20 companies, which have high liquidity and large market capitalization, chosen from various fields of Tehran Stock Exchange. The used historical price data was adjusted through factors of dividends and capital increase. Computational tests and simulations indicate that our hybrid method clearly offers higher profits and lower losses than those of genetic network programming method with rule accumulation (GNP-RA) on all stocks and those of the Buy&Hold strategy on 17 out of 20 instances (85%), especially when the price uptrends are sharp.

Public Dissentiment: Hacktivism in the Age of High-Frequency Traders

Public Dissentiment (2016), an artwork by this author, is an artistic research project and an online tool that helps protestors negatively impact the price of a publicly traded stock by applying the same sentiment analysis tools used by stock trading algorithms. In contemporary stock markets, high frequency trading bots that continuously buy and sell stocks, increasingly perform the role of market makers. These bots provide the market with liquidity by usually being ready to take the buy or sell end of a stock trade. However, these algorithms are also reading news stories and social media posts at lightning speed, and when they detect uncertainty they will pull out of the market causing a stock, or group of stocks, to drop rapidly in value. This text examines the technical and legal aspects of the contemporary networked stock exchange system that make Public Dissentiment’s intervention possible and provides a context for these factors through an analysis of how they produced a space for artistic intervention. Locating this work in the frames of hacktivism, tactical media, and other historical precedents for both network and financial interventions further contextualizes this project. Public Dissentiment falls into the category of mass action hacktivism, which is closely associated with tactical media and culture jamming, and typically includes a performative element. The application is a prototype for how protestors can leverage technology in the age of networks.

Benchmarking the World: A Proposal for Regulatory Oversight of Stock Market Index Providers

Wall Street has recently seen a shift from active management, where investors or portfolio managers buy and sell stocks, towards passive management, where investors invest in funds that seek to match the returns of an underlying index. As the popularity of index funds has grown, questions have arisen regarding the role of the index providers that produce the underlying indices. Unlike the funds themselves, these providers are currently not regulated by the U.S. Securities and Exchange Commission. This wide discretion may allow index providers to take control of the market, since they have the ability to control investors’ exposure to different countries’ markets. The role of index providers also raises concerns about investor transparency and market manipulation in the wake of the 2012 LIBOR manipulation scandal. This Note argues that the U.S. investment industry should require index providers to register with the SEC and to solicit comments from the public through notice-and-comment periods when the providers add new rules or modify existing rules.

OPTIMAL INVESTMENT STRATEGY WITH DIVIDEND PAYING AND PROPORTIONAL TRANSACTION COSTS

A Markowitz’s mean-variance investment strategy is studied in a market with a stock, a bond, dividend payment and proportional transaction costs. Two control variables, portfolio strategy and dividend are considered in this paper. The control variables are inherent with a finite time horizon. This paper aims at minimizing the investment portfolio risk and maximizing the dividend process of the investment over time subject to portfolio allocation strategy, expected net wealth and investment costs over time. The method of Lagrangian multiplier was adopted. As a result, the optimal portfolio and optimal dividend payment are obtained. We found that the optimal portfolio process depends on the optimal dividend payment over time. Also, obtained in this paper, is the optimal portfolio with no dividend payment. We found that for the investment to achieve high target, more of the fund should be invested in stock and low dividend should be declared. The region of boundedness for stock purchase and stock sale are established. We found that for the investment to break-even, the quantum amount of stock sold must be greater than the quantum amount of stock purchase over time. The efficient frontier of the investment strategy was also obtained.

The Relationship between Social Responsibility and Majority Shareholders’ Stock Selling: Considering the Mediation of Investors Tendency

<p><em>Based on the research object of majority shareholders’ stock selling at the post period of equity division reform, this paper takes 8019 A-share listed companies in Shanghai and Shenzhen stock market from 2007-2013 as sample, empirically analysis the relationship between social responsibility and majority shareholders’ stock selling, and consider mediation effect of the investor tendency. The study found that from the point of internal governance effect, the better of social responsibility, the lower possibility of majority shareholders’ stock selling. At the same time, the lower of market reduction premium, the lower possibility of majority shareholders’ stock selling, after considering the influence of investors’ tendency, the inhibition effect of social responsibility will be magnified, which means securities market identity with the inhibition effect that comes from the social responsibility caused by listed companies’ internal. Further study found that majority shareholders will avoid stock selling during the period of the social responsibility reporting, and market investors will hold different attitudes toward majority shareholders for their first and continuous stock selling, and on their view, continuous stock selling is a “bad” event.</em></p>

Predicting Stock Market: An Approach with Artificial Intelligence

Artificial Intelligence (AI) technology has advanced impressively since inventors began tampering with its potential. Many believe that the next great use for AI technology will be in the field of financial market speculation. Technology can be used either to make our lives better or make money. The stock exchange market is the most volatile and most dynamic of all. Special care has to be exercised in buying and selling of stocks from different companies or businesses. The probability of losing the stocks and acquiring benefits through the stocks are fifty-fifty. Volatility of the stock market jumbles up a trader’s nervous system making it difficult to understand or thin rationally. Artificial Intelligence is supposed to be a predictive model that looks at more than technical patterns of trading. It has the ability to identify financial features of companies (e.g. price to earnings ratio, long term (business loans) that will make money in the long run. This requires capabilities from different areas of study and massive computational power which is why it is only prevalent in recent years. This paper tries to attempt of coming up with a basis and prediction using Artificial Intelligence in identifying trading pattern relations which appropriately inter relates with High Frequency Stock Trading based on pre-set criteria

A production inventory model with selling price and stock sensitive demand under partial backlogging

In the proposed paper we develop an inventory model for instantaneous deteriorating items with multi variate function of demand rate. Generally, we observe that the demand for any product depends on so many factors, out of which the available stock and selling price are the main factors. So in this paper we have assumed that the demand rate is a function of stock and selling price. The production rate is taken as a function of demand rate. The shortages are allowed and it is assumed that the occurring shortages will be backlogged for which the backlogging rate is a function of waiting time. The numerical example and sensitivity exploration with respect to various parameters are also cited to illustrate the study.

When Trading is Hazardous to Your Wealth: Financial Sophistication and Stock Selling During the 08/09 Financial Crisis

We study the motivations for stock selling in a context where this was (at least from an ex-post perspective) hazardous to investors' wealth. Making use of a unique feature of the Cognitive Economics Study (CogEcon) that allows us to elicit domain-specific stock literacy and corresponding confidence, we find that independent of their actual knowledge, investors were more inclined to sell stocks during the financial crisis with high levels of confidence. This was the case even when trading was apparently not motivated by liquidity demands, risk-bearing capacities or expectations about future asset market developments. Even more importantly, and in line with the literature that relates the discrepancy between financial knowledge and confidence to making investment mistakes, we find that overconfidence was associated with a higher propensity to realize stock losses - that is, selling assets whose value had deteriorated. Male as well as female investors in our sample were overconfident on average shortly before the outbreak of the crisis. Strikingly, however, only female investors traded to their detriment as a result of their overconfidence. We do not find, in contrast, male investors to be at a real disadvantage of high levels of confidence not warranted by actual knowledge. We take a number of steps in response to endogeneity concerns including an alternative identification method recently introduced by Lewbel (2012) that exploits information from the heteroscedastic structure of the data. Further research is needed to understand the long-term consequences of our findings concerning stock-market participation and finally wealth levels of females, who are already at a high risk of hitting poverty at old-age.

Investor recognition and stock returns: evidence from China

PurposeMerton’s model of capital market equilibrium under incomplete information predicts that contemporaneous stock returns are positively related to investor recognition and that future stock returns are negatively related to investor recognition. The purpose of this paper is to empirically investigate whether Merton’s theory holds true for the Chinese stock market.Design/methodology/approachThis paper proposes the degree of shareholder base growth (SBG) as a proxy for investor recognition and examines the relationship between investor recognition and stock returns through a univariate analysis and Fama-Macbeth cross-sectional regressions based on A-Share listed firms.FindingsThe results show that investor recognition is nonlinearly and positively related to contemporaneous stock returns and is negatively related to future stock returns in contrast to the conclusions of Merton’s theory. A long-short trading strategy that involves buying stocks with the lowest SBG rate and that sells stocks with the highest SBG rate will earn an average monthly return of 3.615 percent.Research limitations/implicationsThough Merton’s theory is not fully reflected in the Chinese stock market, investor recognition is considered an important risk factor in the Chinese stock market.Originality/valueNo works have yet investigated the validity of Merton’s “investor cognition hypothesis” in relation to the Chinese stock market. This paper strives to fill this gap.

PROFITABILITY OF CAPM MOMENTUM STRATEGIES IN THE US STOCK MARKET

This paper provides a historical review of the performance of the risk-adjusted momentum strategies when buying and selling stocks according to the alpha estimates of the CAPM and Fama–French regressions. Our sample covers over 60 million US daily firm-return observations. High Sharpe ratios are obtained under our risk-adjusted strategies. It is also found that stock market crashes have no apparent impact on our momentum profits. Keywords: Momentum Strategies; Sharpe Ratio; Fama-French Model; CAPM Model.

The Memory of Stock Return Volatility: Asset Pricing Implications

We examine long memory volatility in the cross-section of stock returns. We show that long memory volatility is widespread in the United States and that the degree of memory can be related to firm characteristics, such as market capitalization, book-to-market ratio, prior performance, and price jumps. Long memory volatility is negatively priced in the cross-section. Buying stocks with shorter memory and selling stocks with longer memory in volatility generates significant excess returns of 1.71% per annum. Consistent with theory, we find that the volatility of stocks with longer memory is more predictable than stocks with shorter memory. This makes the latter more uncertain, which is compensated for with higher average returns.

Short selling around the expiration of IPO share lockups

Abstract We are the first to examine daily short selling activity around the expiration of IPO share lockups. We find that short selling increases before the lockup expiration date and declines afterward, and the level of short selling is higher in stocks of venture capital (VC)- and private equity (PE)-backed IPOs than other IPOs. Unlike VC-backed IPO stocks, PE-backed IPO stocks do not experience a negative return or a trading volume jump on the lockup expiration date. PE investors do not reduce percentage ownership in the IPO firm as much as VC investors do after lockup expirations. Short selling in PE- and VC-backed IPO stocks prior to the lockup expiration date can predict PE and VC ownership reduction but not the stock returns after the lockup expires. In contrast, short selling in stocks of the IPO firms without a PE or VC investor can predict stock returns after the lockup expires.

Weekly Momentum Study on Indian Stock Markets

The Efficient Market Hypothesis (EMH) has been one of the dominant topics in the financial research literature. The main purpose of this study is to explore the existence of return continuation in the Indian Stock Markets, thus investigating its efficiency at the weak form level (Fama,1970). Momentum strategies which buy stocks that have performed well in the past and sell stocks that have poor performances previously – generate significant positive returns. The weekly momentum strategies can be executed by the investor to generate significant profits. The explanation of momentum remains challenge in literature. The behavioral factors may account for the momentum phenomena.

Selling Winners or Losers: Two-Stage Decision Making and the Disposition Effect in Stock Trading

The disposition effect is regarded as a property of an individual stock: If an investor has made a loss on a stock, he or she is less likely to sell it, whereas if an investor has made a gain on a stock, he or she is more likely to sell it. This means that the more stocks in a portfolio are in gain, the greater the probability that the stock sold will be in gain. But we show that the disposition effect is, in the large part, a gain-loss domain level decision, where investors first decide whether to sell a stock in the domain of gains or losses, and only then choose a stock to sell from within their chosen domain. The signature of this two-stage model is that the probability that the stock sold will be in gain is constant across portfolios with different numbers of stocks in gain and in loss. We see this pattern very clearly in two independent stockbroking datasets, one from the US investors from 1990s and another from UK investors from 2010s. We also see the pattern in a stockbroking experiment where we randomly vary the number of gains and losses in a portfolio. We conclude that sell decisions are taken at the gain-loss domain level, not just at the individual-stock level.

Do Foreign Investors Better Seize the Moment of Buying and Selling Stocks? An Empirical Investigation Based on SH-HK Connect Scheme

We empirically investigate the Granger causality between northbound SH-HK Connect and domestic market behavior using a newly proposed sequential multiple-horizon non-causation test strategy. Empirical results illustrate that the northbound SH-HK Connect only cause onshore market change in very few windows, which means that foreign investors do not better seize the moment of buying and selling than domestic investors. Given the low proportion of the Qualified Foreign Institutional Investors (QFII) and the northbound Stock Connect quotas in aggregate free float A-share market cap and much lower turnover velocity than individual A-share investors, domestic investors would remain a dominant marginal price setter in the onshore market until foreign ownership reaches more meaningful levels.

How the Australian Ban on Short Selling During the GFC Affected Market Quality and Volatility

We examine the effects of the short selling ban, imposed by Australian regulators in the wake of the global financial crisis, on trading of financial stocks. Unlike other developed markets, where regulators imposed short-selling restrictions for brief periods of time at the height of the financial crisis, the ban on short selling of financial stocks on the Australian Stock Exchange lasted eight months, including both the tumultuous end of 2008 and the calmer period up to May 2009. Our control group consists of matched Canadian financial institutions which were unaffected by a short selling ban. We analyze the impact of the imposed short selling constraints on measures of market quality and on stock prices using univariate and multivariate fixed effects panel regressions. As predicted by previous theoretical work, we find that stocks subject to the short selling ban suffered a severe degradation in market quality. Controlling for the adverse effects of the financial crisis on financial markets, we show that imposing constraints on short-selling reduced trading activity, increased bid and ask spreads and increased intraday volatility. Moreover, there appears to be no evidence for lasting price support from the restrictions.

Information Contents of Short Selling Restriction and Stock Lending and Borrowing Transactions

This study focuses on the distinctive incident in which Stock Lending and Borrowing (SLB) transaction and short selling grew continuously even during the wavering period of the Korea Composite Stock Price Index (KOSPI) in the Korea Exchange (KRX) market. The authors analyze the information contents of SLB and short selling on KOSPI financial stocks. The financial stocks were freed from short selling ban on November 14, 2013, after being banned for more than five years following the global financial crisis in 2008. Employing the event study methodology, the authors also analyze the differences between stock market returns before and after the short selling ban. The results of this study show that the concentrated group including high levels of SLB and short selling were characterized by the higher Abnormal Return (AR) with negative effect than the comparative group after the occurrence of short sales. The results also discover that SLB and short selling transactions provide the market with significant information contents. The information effect of SLB and short selling is consistent over time, but is stronger when the transactions are more concentrated. Finally, the results reveal that the short selling has the most considerable influence on the stock market.