selling strategies
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This paper provides an in-depth analysis machine study of the relationship between stock prices and indices through machine learning algorithms. Stock prices are difficult to predict by a single financial formula because there are too many factors that can affect stock prices. With the development of computer science, the author now uses many computer science techniques to make more accurate predictions of stock prices. In this project, the author uses machine learning in R Studio to predict the prices of 35 stocks traded on the New York Stock Exchange and to study the interaction between the prices of four indices in different countries. Further, it is proposed to find the link between stocks and indices in different countries and then use the predictions to optimize the portfolio of these stocks. To complete this project, the author used Linear Regression, LASSO, Regression Trees, Bagging, Random Forest, and Boosted Trees to perform the analysis. The experimental results show that the MRDL deep multiple regression model proposed in this paper predicts the closing price trend of stocks with a mean square error interval [0.0043, 0.0821]. Additionally, 80% of the proposed DMISV, KDJSV, MACDV, and DKB stock buying and selling strategies have a return greater than 10%. The experimental results validate the effectiveness of the proposed buying and selling strategies and stock price trend prediction methods in this paper. Compared with other algorithms, the accuracy of the algorithm in this study is increased by 15%, and the efficiency of prediction is increased by 25%.

This paper addresses an optimal stock liquidation problem over a finite-time horizon; to that end, we model it as an optimal stopping problem in a regime-switching market. The optimal stopping time is written as a solution to a system of Volterra type integral equations. Moreover, it reveals that when the risk-free interest rate is always lower than the return rate of the stock, it is never optimal to sell the stock early; otherwise, one should sell the stock in bear market if the stock price reaches a critical value and hold the stock in bull market until the maturity date. Finally, we present a trinomial tree method for numerical implementation. The numerical results are consistent with the theoretical findings.

Abstract This article suggests that the illuminated initials and borderwork added to ten early printed books in England are attributable to a single illuminator, the ‘Incunables Limner’, an individual for whom there is circumstantial evidence that he specialized in the illumination of printed books. Five of these books are copies of William Caxton’s Golden Legend (Westminster, 1483–84), while the other five are Continental imprints (two from Strasbourg, and one from each of Basel, Verona, and Parma) printed between 1476 and 1484. In addition, a second illuminator can be identified in a sixth copy of Caxton’s Golden Legend, working to the same design as that employed within the five copies decorated by the Incunables Limner. The possibility is considered that books illuminated by the Incunables Limner were products of Caxton’s overseas trade, and that it was through acting in this capacity that the artist’s specialization was viable. The Continental books are explored for what they might imply about Caxton’s wider book-selling strategies, and three with evidence for early English ownership are selected for particular attention.

AbstractThis paper considers a retailer who sells perishable fresh products directly to customers through an online channel and encounters a transportation disruption. Products shipped during the disruption period come with an uncontrollable delivery lead time, resulting in product quality degradation. To balance the compensation price provided to customers because of quality losses, the retailer might employ freshness-keeping efforts to reduce the quality loss during transportation. Therefore, it raises several fundamental questions for the retailer in mitigating the disruption. Is it always optimal to satisfy those customers who are willing to purchase during disruption? If it is profitable to fulfill orders along with an extra delivery lead time, and with a quality loss compensation, what is the optimal freshness-keeping effort? If it is preferable to deliberately create unsatisfied demand by announcing shortages (rationing) to customers, when is the optimal time to do so? To answer these questions, we first present the dynamics of post-disruption inventory and demand, taking into account the demand learning effect facilitated from negative word-of-mouth during disruption and the demand recovery after disruption ends. Afterward, we develop a model to achieve the optimal selling strategy for maximizing post-disruption profit, identifying the joint decision of the rationing period and freshness-keeping effort. Finally, by numerical analysis, three types of selling strategies are visually provided to hedge against disruptions of different lengths.

In the field of strategic marketing, the recency, frequency and monetary (RFM) variables model has been applied for years to determine how solid a database is in terms of spending and customer activity. Retailers almost never obtain data related to their customers beyond their purchase history, and if they do, the information is often out of date. This work presents a new method, based on the fuzzy linguistic 2-tuple model and the definition of product hierarchies, which provides a linguistic interpretability giving business meaning and improving the precision of conventional models. The fuzzy linguistic 2-tuple RFM model, adapted by the product hierarchy thanks to the analytical hierarchical process (AHP), is revealed to be a useful tool for including business criteria, product catalogues and customer insights in the definition of commercial strategies. The result of our method is a complete customer segmentation that enriches the clusters obtained with the traditional fuzzy linguistic 2-tuple RFM model and offers a clear view of customers’ preferences and possible actions to define cross- and up-selling strategies. A real case study based on a worldwide leader in home decoration was developed to guide, step by step, other researchers and marketers. The model was built using the only information that retailers always have: customers’ purchase ticket details.

The rapid development of skin care products (Skincare) through social media has changed the paradigm of the digital marketing model in general. This article discusses digital marketing models that skin care products use in marketing their products. The method used was Krippendorff content analysis which was carried out through digital observation on the Instagram account of skin care products (Skincare) Elvicto @ elvicto.id from March 1, 2021 to March 31, 2021. The results of this study indicate that Elvicto is more engaged in a digital marketing communication strategy through marketing. Instagram @ elvicto.id which is dominated by personal selling strategies.

Direct Selling has become a veritable part of business success story particularly in the deposit money bank of Nigeria. The main objective of this study is to examine the effect of direct selling strategies on customers loyalty in the Nigerian deposit money banks. Primary data was used for the study. The data were collected through a structured questionnaire. The study adopted the cross-sectional survey research design method. The population of the study was 812 employees from selected deposit money banks (Zenith Bank, GTBank, UBA, First Bank and Fidelity Bank) in Delta State. A total number of 260 respondents were selected as the sample size. To validate the instrument, face and content validity were adopted. To verify the reliability of the research constructs, the internal consistency analysis and item-to-total correlation was applied to identify the internal consistency reliability of the constructs. Descriptive statistics were used to analyze the responses of the respondent background profile and also to reveal the pattern of responses from each of the constructs in the sub-scale. Multiple regression analysis was adopted to analyze the effect of the variables of direct selling strategies on customers loyalty.

We study the power of selling opaque products, that is, products where a feature (such as color) is hidden from the customer until after purchase. Opaque products, which are sold with a price discount, have emerged as a powerful vehicle to increase revenue for many online retailers and service providers that offer horizontally differentiated items. In the opaque selling models we consider, all of the items are sold at a single common price alongside opaque products that may correspond to various subsets of the items. We consider two types of customers, risk-neutral ones, who assume they will receive a truly random item of the opaque product, and pessimistic ones, who assume they will receive their least favorite item of the opaque product. We benchmark opaque selling against two common selling strategies: discriminatory pricing, where one explicitly charges different prices for each item, and single pricing, where a single price is charged for all the items. We give a sharp characterization of when opaque selling outperforms discriminatory pricing; namely, this result holds for situations where all customers are pessimistic or the item valuations are supported on two points. In the latter case, we also show that opaque selling with just one opaque product guarantees at least 71.9% of the revenue from discriminatory pricing. We then provide upper bounds on the potential revenue increase from opaque selling strategies over single pricing and describe cases where the increase can be significantly more than that of discriminatory pricing. Finally, we provide pricing algorithms and conduct an extensive numerical study to assess the power of opaque selling for a variety valuation distributions and model extensions. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.