Selling Decisions Research Articles

Mastering Market Movements: Technical Indicators in IDX30 Stock Selection

This study aims to analyze the role of trading volume, foreign money flows, and technical chart patterns in shaping investment decisions for IDX30 companies. Utilizing descriptive quantitative research, data was collected from Stockbit and IPOT, focusing on stock price charts, trading volume, and broker summaries from January 1, 2020, to December 31, 2022. The findings reveal that purchase decisions were made when stock prices broke out of a chart pattern, accompanied by increased trading volume and foreign accumulation. Conversely, sell decisions occurred when stock prices broke down from a pattern, followed by increased volume and foreign distribution. These results underscore the importance of technical analysis in guiding informed investment decisions, helping investors avoid irrational practices and potential losses. The study highlights the practical need for investors to integrate technical indicators, such as volume and foreign flows, into their decision-making processes to enhance investment outcomes.

Agent-Based Modeling and Simulation for Farmer Decision Making

Shallots are a highly demanded, perishable commodity whose fluctuating prices pose significant risks to farmers, affecting their income stability. This research aims to model and simulate the selling decisions of shallot farmers using Agent-Based Modeling (ABM). The model represents the individual behaviors of farmers and buyers, focusing on decisions to either delay or immediately sell harvested shallots. Key variables such as market prices, inventory levels, and profit expectations were incorporated into the model. Primary data were collected from observations of shallot farmers in Nganjuk, East Java. In contrast, secondary data were sourced from the Indonesian Central Statistics Agency (BPS). The simulation results indicate farmers maximize their profits by selling shallots when the minimum sales profit margin reaches 20%. Farmers tend to postpone sales when market prices fall by drying the shallots, reducing market supply, and elevating prices. The study concludes that delaying sales can be an effective strategy to stabilize prices and enhance profitability, particularly when dried shallots command higher prices than fresh ones. These findings offer valuable insights for developing agricultural policies to optimize farmers' income while mitigating price volatility risks.

Indonesian Stock Price Prediction Using Neural Basis Expansion Analysis for Interpretable Time Series Method

The stock market is an attractive investment venue for many individuals and companies. However, unexpected share price fluctuations can cause significant financial losses. In stock investment, predicting stock price movements is the most frequently discussed topic because it allows investors to make the right investment decisions to make big profits. Therefore, a model is needed to predict future stock prices, one strategy for maximising investment profits. New state-of-the-art deep learning architectures for time series forecasting are being developed yearly, making them more accurate than ever. The most commonly used network for such a solution is Long Short-Term Memory (LSTM) architecture, but it has limitations such as long training time and interpretability. This study aims to evaluate another state-of-theart solution, Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), in comparison with LSTM by utilising historical data of PT Bank Central Asia Tbk (one of the banking companies in Indonesia) from 25 March 2013 to 21 March 2023. N-BEATS is a relatively new variable method that can produce accurate predictions using neural networks. This architecture has advantages such as interpretability, seamless applicability across diverse target domains without requiring modifications, and fast training. Based on tests carried out with prediction errors measured using the Mean Average Percentage Error (MAPE), it was found that the N-BEATS model outperformed the LSTM model with a MAPE value of 1.05 percent. In conclusion, this research shows the use of a new method of deep learning algorithms to predict stock prices, which contributes to facilitating stock buying and selling decisions by investors.

Lottery jackpot winnings and retail trading in the neighborhood

We use lottery jackpot winnings in a neighborhood as an exogenous shock to investing in stocks. We find that retail investors whose brokerages are near the stores that sell winning tickets buy more stocks than their counterparts after the shock. These purchases lead to lower returns. We use a survey to identify the behavioral factors like regretfulness and probability weighting that are the main drivers of our findings. Moreover, these investors tend to buy more lottery-like stocks, but the shock does not affect their selling decisions. Finally, we perform several falsification tests and robustness checks and find consistent results.

Algorithmic stock trading based on ensemble deep neural networks trained with time graph

Financial forecasting is generally implemented by analyzing the time series data related to the stock. This is accomplished widely with deep neural networks (DNNs) since DNNs can directly extract the related information that is otherwise hard to obtain. Time series is the core data representation of financial forecasting problem since it comes naturally. However recent studies show that even if time series representation is necessary, it still lacks certain aspects related to the problem. One of them is the relationship between the stocks of the market which can be captured through graph representation. Therefore, DNNs might solve the financial forecasting problem better when graph and time series representations are combined. In this study, we present different graph representations that can be used for this purpose. We also present an ensemble network that gives an investment strategy related to the stock market from stock predictions. Our proposed model returns an average of 20.09% annual profit on DOW30 dataset through daily buy–sell decisions based on close prices. Therefore, it can serve as a daily financial investment strategy, offering higher annual returns than conventional heuristic approaches.

The effect of oil price on stock market performance and petrochemical stock value using NARDL

The aim of the study is to examine the impact of oil prices on the stock market performance and the stock value of petrochemical companies on the Tehran Stock Exchange (TSE). For this, it utilizes the Non-linear autoregressive distributed lag during 2011 to 2021 using time series data with monthly frequency. The results showed that in the short run, the oil price has an asymmetric impact on the stock value of petrochemical industries and also on the performance of the stock market, while in the long run, this effect is symmetrical. So, the influence of bouncing up the oil price is greater than the impact of its moving down. Then, the asymmetric effect happens through the increase in oil prices. Therefore, ups and downs in oil prices lead to fluctuations in the stock market returns, especially in petrochemical companies. Based on the results, capital market participants should watch the informational content of oil price fluctuations to make buying and selling decisions and invest in companies' shares according to these fluctuations.

Should competing sellers adopt the data analytics service to enable product improvement in online marketplace

More and more marketplace platforms choose to use the data gathered from consumers (e.g., customer search terms, demographics) to provide a data analytics service for third-party sellers, both encouraging innovations and improving the operations of the latter. Through adopting the data analytics service, a seller can enhance its competitiveness by improving the quality of its products when there is more than one seller offering substitutes. This study develops a game-theoretic model to characterize a scenario with a marketplace platform and two competing sellers that sell substitutable products and decide whether to adopt the data analytics service provided by the platform. Then, we find that a seller's decision of whether or not to purchase the service depends on the competitor's decision and the two sellers' absorptive capacities of knowledge. Furthermore, a seller can benefit from adopting the data analytics service only if the seller has a high absorptive capacity, which can help increase product quality. When only one seller adopts, the platform and competing sellers can benefit from the adoption if this seller's absorptive capacity is high and the other's is moderate. The sellers' interests and social welfare can be aligned unless both sellers' absorptive capacities are low or high.

Behavioral factors influencing investment decision-making in the Nigerian real estate stock market

This paper examines the behavioral factors that influence investment decision-making in the Nigerian property stock market, with a survey of registered stockbrokers with the Nigeria Exchange Group (NGX), to provide information for optimal investment decision-making. The study adopted a direct survey method. Relevant information was obtained from registered Nigerian stockbrokers via a closed-end questionnaire, and their responses were analyzed using principal component factor (PCF), and ordinary least square (OLS) regression analyses. The study confirms the influence of behavioral factors on investment decisions in the real estate stock market. Buying decisions dominate during bull markets, but investors prefer selling decisions when the market is down. Six behavioral principal factors were extracted. Investor responsiveness factor explains the dynamics of investment decisions in the optimistic property stock market, while herd effect factor predicts investors' investment behavior in the pessimistic market. The empirical evidence of emotional trading underscores the importance of behavioral factors in achieving significant property stock market performance and optimal investment decision making in the Nigeria real estate stock market.

Restaurants’ Platform Partnership for Social Promotion and Resilient Revenue: Is Reward-Based Traffic Really Rewardful?

Restaurants have traditionally operated offline only, but the growth of food delivery platforms has prompted a shift toward online sales. In practice, consumers who share digital coupons offered by the platforms (e.g. Uber Eats and Meituan) in social networks (e.g. Facebook, Twitter, and WeChat) will be rewarded for the social traffic, which effectively attracts many restaurants to open online store. However, this also leads to intensified competition with the restaurant's physical (offline) store. In this article, we formulate the restaurant's tradeoffs among platform traffic benefits, consumers’ heterogeneous utility, and the platform's commission in the online selling decision. Interestingly, we find that the increased platform traffic may be harmful to the restaurant, and even the entire channel system. The platform offering high subsidies may trap restaurants in a pricing dilemma. We also find that restaurants’ online selling will induce a negative externality due to online/offline order congestion, but it will not qualitatively change the main findings.

Stock Price Prediction of GM Company: Comparison Based on KNN, Linear Regression and LSTM

Stock price prediction holds significant importance in the financial sector. It not only aids investors in making informed buy and sell decisions to achieve potential profits but also supports enterprises and financial institutions in risk assessment and management. This study utilized the stock prices of GM stocks spanning from 2013 to 2023 as dataset, with the goal of predicting their closing prices. This paper carefully identifies the parameters in the three model and one indicator of RMSE outcomes were computed. Visualizations of the results of the three model predictions are also provided. After comparing the indicator of the three models, it is found that the RMSE for LSTM is the smallest, indicating that the LSTM outperforms the KNN and linear regression from the perspective of forecast accuracy. The research highlights the application of the machine learning algorithm of LSTM in the prediction of the prices of financial assets and its practical value in financial market analysis.

Stochastic Cyclic Inventory Routing with Supply Uncertainty: A Case in Green-Hydrogen Logistics

Hydrogen can be produced from water, using electricity. The hydrogen can subsequently be kept in inventory in large quantities, unlike the electricity itself. This enables solar and wind energy generation to occur asynchronously from its usage. For this reason, hydrogen is expected to be a key ingredient for reaching a climate-neutral economy. However, the logistics for hydrogen are complex. Inventory policies must be determined for multiple locations in the network, and transportation of hydrogen from the production location to customers must be scheduled. At the same time, production patterns of hydrogen are intermittent, which affects the possibilities to realize the planned transportation and inventory levels. To provide policies for efficient transportation and storage of hydrogen, this paper proposes a parameterized cost function approximation approach to the stochastic cyclic inventory routing problem. Firstly, our approach includes a parameterized mixed integer programming (MIP) model which yields fixed and repetitive schedules for vehicle transportation of hydrogen. Secondly, buying and selling decisions in case of underproduction or overproduction are optimized further via a Markov decision process (MDP) model, taking into account the uncertainties in production and demand quantities. To jointly optimize the parameterized MIP and the MDP model, our approach includes an algorithm that searches the parameter space by iteratively solving the MIP and MDP models. We conduct computational experiments to validate our model in various problem settings and show that it provides near-optimal solutions. Moreover, we test our approach on an expert-reviewed case study at two hydrogen production locations in the Netherlands. We offer insights for the stakeholders in the region and analyze the impact of various problem elements in these case studies. Funding: This project received funding from the Fuel Cells and Hydrogen 2 Joint Undertaking (now Clean Hydrogen Partnership) under [Grant Agreement 875090]. The Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme, Hydrogen Europe and Hydrogen Europe Research. A. H. Schrotenboer received support from the Dutch Science Foundation (Nederlandse Organisatie voor Wetenschappelijk Onderzoek; NWO) through [Grant VI.Veni.211E.043].

Upward Mobility Bias in the Selling Decisions of Retail Traders

Upward Mobility Bias in the Selling Decisions of Retail Traders

OEM‐servicizing with a multiusecycle product: Model analysis and insights

Customers of many original equipment manufacturers (OEMs) in business‐to‐business markets today demand product servicizing in which instead of buying the product from the OEM they buy the use of a product during a lease. During the lease, (i) the customer uses the product and returns it to the OEM after the use reaches a specific level, (ii) the OEM remanufactures the product in a costly process and sends it back to the customer, and this usecycle is repeated multiple times. The lease terms typically involve a use‐based payment. While a greater product use by a customer brings a larger revenue to the OEM, it also increases the remanufacturing cost incurred by the OEM. We investigate this trade‐off using an analytical model for a contract that is used extensively in industry. For the case of homogeneous customers with a common use rate of the product, we optimize lease payment terms and identify market and product‐remanufacturing characteristics for which the OEM should servicize the product instead of selling it. We show that an OEM should not servicize a product when the customers' use rate exceeds a threshold. This is because, beyond this threshold, the remanufacturing cost increases disproportionably, exceeding the higher usage‐based revenue. Subsequently, we consider a market with two segments with different use rates. We consider two servicizing modes: (i) servicize both market segments or (ii) selectively servicize only one segment and sell the product to the other segment, and the default mode of selling in both segments. We develop optimal lease payment terms for these use‐based servicizing modes, identify thresholds of product and market characteristics for the optimality of these modes. Finally, we extend the results to a market with more than two segments and compare the environmental impacts of the servicizing or sell decision. Numerical results informed by empirical data show that the OEM's loss of profit from choosing a suboptimal servicizing/sell decision can be significant.

Forecasting US Stock Prices Through Sentiment Analysis and Machine LearningA Case Study of Tesla Inc.

The financial sector is characterized by high volatility, and the accurate forecasting of stock prices is an actively pursued area of research and analysis. This study extends the scope of machine learning techniques, such as Artificial Neural Networks and fuzzy-based techniques, to enhance the precision of stock price predictions. The central focus of this research is algorithmic trading, which combines various qualitative factors in stock buying and selling decisions. More specifically, this study delves into the unique relationship between Elon Musks tweets and Teslas stock value. To identify patterns in the pre-processed dataset, which has had stop words removed, exploratory data analysis is used as the primary research methodology. The study conclusively demonstrates that a positive correlation exists between the number of tweets/engagements and Teslas closing price, and this correlation holds true in reverse: a decrease in tweets/engagements corresponds with a decrease in Teslas closing price. This research has broader implications for macroeconomic analysis of the US stock market by highlighting the role of technology and innovation in financial markets, as well as the importance of data-driven approaches in economic

Business ownership and the secondary market

I combine survey and transaction data to empirically characterize secondary markets for wholly owned businesses in the United States. While markets are active, with an estimated 2.9% of businesses sold annually, they are also frictional, with the median sale taking between 170 and 200 days. To quantify the impact of market frictions on business formation, ownership, continuation, and entrepreneurial welfare, I estimate a search and matching model of business ownership and resale using novel data from an online market. In the estimated model, removing secondary markets reduces output by almost 21%, while firm entry rates rise by 2.5 percentage points, highlighting a trade-off between firm entry and continuation that is absent in models without resale. I then characterize the planner's problem and show that buying and selling decisions will generally never be simultaneously efficient. A transaction tax used to finance a minimum income scheme for entrepreneurs raises welfare by 0.94%. Finally, I extend the model to include founding risk and find the risky economy is five times more responsive to frictions relative to the benchmark economy.

A Technical Indicator for a Short-term Trading Decision in the NASDAQ Market

Purpose: The objective is to employ a stochastic model to develop a new technical analysis indicator that could compute the variation of any index. We demonstrate the superiority and applicability of our proposed model and show that our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions by using our proposed model to analyze the variation of the NASDAQ Composite Index (IXIC). Design/methodology/approach: This study uses a stochastic process without mean-reverting property to develop a stochastic model that could compute the variation of any index. To show the superiority and applicability of our proposed model in computing the variation of any index, we employ our proposed model to compute the daily closing values of the IXIC over 10 years and derive the variation of the IXIC index. Findings: Our findings indicate that, based on the mean absolute percentage error, the calibrated model we proposed provides a more accurate estimate of the short-term index that outperforms both the simple moving average and the MACD in predictive accuracy. It delivers a robust anticipation of the overall market trend by offering a 95% confidence interval for the value of the composite NASDAQ index. Practical Implications: Our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions. Our proposed model provides market analysts with a forecasting tool by using our proposed technical analysis indicator to anticipate the market trend, which outperforms some traditional indicators of technical analysis, including Simple Moving Averages and Moving Average Convergence Divergence. Originality/value: Our approach, results, and conclusions are original and new in the literature. Our proposed model is a new technical indicator for predicting any index based on a stochastic process, which has been found to outperform some classical indicators. This research makes significant contributions to the field of decision sciences because the indicator we have developed plays a crucial role. It enables better buying and selling decisions based on market trend predictions estimated by using our proposed model. In this way, the indicator offers added value to professionals in making investment decisions. The results of this research work contribute to the development of new technical analysis indicators. Here, the IXIC index is an example, the use of this indicator is wider and could concern any stock market index and any share. So, this work enriches the literature and opens up new avenues for any researcher who wants to use stochastic processes to develop new technical indicators for different financial assets.

Managing demand by upgrade programs and markdown pricing with a product rollover

AbstractIt has been challenging for firms to effectively manage demand when they release products of one generation after another one. Motivated by the observations from the smartphone industry, this paper investigates the effectiveness of two demand management strategies in the presence of a product rollover: the upgrade program and price markdown policy. Under an upgrade program, a firm allows customers to upgrade their on‐hand product to a new generation product that will be released in a future time. Under a markdown pricing policy, the firm offers a discount for the currently available product so as to induce waiting customers to make immediate purchases. The two demand management strategies target different groups of customers and have distinct impacts on customers' choices. Starting from the time‐varying choice behavior of a heterogeneous group of customers, we study the optimal pricing decisions involved in the two strategies. Specifically, when customers are myopic in the sense that they only make a one‐time purchasing decision upon arrival, we show that the firm should offer the upgrade program only when the innovation level of the new product is relatively high, and the firm's optimal upgrade price can increase over time. Generally, the firm should offer the upgrade program during the early selling period and adopt markdown pricing as the release date of the new product approaches. Numerical experiments reveal that the dynamic upgrade program and markdown pricing policies can help improve profit significantly. When customers are strategic in the sense that they can monitor the selling prices and make dynamic purchasing decisions until they buy a unit of product, we examine two coping strategies that a firm can adopt, and investigate how the strategic monitoring behavior may influence a firm's optimal selling decisions and profit.

Analysts’ Recommendations and Press Sentiment: Complementary or Alternative to Drive Investors’ Trading Behavior?

This paper focuses on the relationship between financial analysts’ recommendations and press sentiment from the perspective of the attention-grabbing theory. Specifically, attention-grabbing should not be enough to explain the effect that media coverage has on investment decisions, since investors are wary of making a mistake and anticipate the regret of a future loss. Our case study pertains to a column reporting on secondhand information and analysts’ recommendations. Once the column did not report the analysts’ advice anymore, we hypothesized investors also assess the sentiment of the column to make sure they are not making a costly mistake. Event studies on abnormal returns and multivariate analyses show that for columns with explicit analysts’ recommendations the attention-grabbing mechanism directs buying decisions while has no influences on selling decision. In the absence of explicit recommendations, investors transform the columns’ content into implicit recommendations leading their buying decisions when the sentiment is highly positive.

Potatoes Storage, Price Spread through Various Channels and Marketing Efficiency in Parwan Province, Afghanistan

This research paper examined the factors influenced farmers' immediate selling of potatoes after harvest and their storage practices, various factors and limitations affected their selling and storage capacity has been identified and the ground conditions as realistic discussed as well as the price spread and marketing efficiency in the three districts Siyagard, Ghorband, and Shinwari of Parwan province, Afghanistan. The study conducted revealed that farmers primarily sold potatoes to pay for household expenses 57.50 percent and finance agricultural inputs 55.00 percent. Moreover, sold for loan repayment 45.00 percent and limited storage space 52.50 percent play significant roles in immediate selling decisions. The study also showed that farmers stored potatoes for various reasons, such as personal consumption 23.3 percent, anticipating higher prices 24.20 percent, meeting off-season demand 23.3 percent, and exploiting competitive prices 23.3 percent. The study provide insights into the price spread in different market channels, indicating varying profit margins for farmers and intermediaries. Finally, the study highlighted the marketing efficiency under different channels, with Channel III showed the highest efficiency 47 percent in direct distribution, followed by Channel II 32 percent and Channel I 29 percent. These findings highlight the complexities of potato marketing in the Parwan province, Afghanistan which can help policymakers to develop a sustainable approach to inform strategies to improve farmers' income and market efficiency.

The Irrelevance of Environmental, Social, and Governance Disclosure to Retail Investors

Using an hourly data set on retail investor individual security positions from Robinhood Markets, we find no evidence that environmental, social, and governance (ESG) disclosures inform retail investors’ buy and sell decisions. The response on ESG press release days by retail investors is indistinguishable from nonevent days. In contrast, these same investors make economically meaningful changes to their portfolios in response to non-ESG press releases, especially those that pertain to earnings announcements. We use stock return tests to show that there is economic content in ESG press releases, and we conduct subsample analyses showing that retail investors do not respond to the most salient and economically transparent ESG disclosures. Collectively, these tests suggest that a lack of economic content, a lack of visibility, and difficulty with investment integration are unlikely to explain our findings. This paper was accepted by Suraj Srinivasan, accounting. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4822 .