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Financial institutions, investors, mining companies and related firms need an effective accurate forecasting model to examine gold price fluctuations in order to make correct decisions. This paper proposes an innovative approach to accurately forecast gold price movements and to interpret predictions. First, it compares six machine learning models. These models include two very recent methods: the eXtreme Gradient Boosting (XGBoost) and CatBoost. The empirical findings indicate the superiority of XGBoost over other advanced machine learning models. Second, it proposes Shapley additive explanations (SHAP) in order to help policy makers to interpret the predictions of complex machine learning models and to examine the importance of various features that affect gold prices. Our results illustrate that the utilization of XGBoost along with SHAP approach could provide a significant boost in increasing the gold price forecasting performance.

This paper presents direct measures of capital costs, equal to the product of the required rate of return on capital and the value of the capital stock. The capital share, equal to the ratio of capital costs and gross value added, does not offset the decline in the labor share. Instead, a large increase in the share of pure profits offsets declines in the shares of both labor and capital. Industry data show that increases in concentration are associated with declines in the labor share.

In 2013, Engelhardt (2013) calculated that the combined power of the top five hundred supercomputers would take approximately 10.5 quintillion years to compute the distribution of eighty thousand heterogeneous goods among six billion consumers, posing a serious practical challenge to the implementation of computerized central planning. Allin Cottrell (2021) calls into question Engelhardt’s assertion, noting that the algorithm used by Engelhardt not only scales poorly but is not even valid for the problem Engelhardt posed. Cottrell offers an iterative algorithm that a one-petaflop machine could use to solve the distribution problem in about five minutes. We correct errors in both Engelhardt and Cottrell, and we offer a way to incorporate production into the problem. The result: modern supercomputers are still not powerful enough to solve central planning’s computation problem.

Motivated by the prevalence of ride-sharing platforms, in “Spatial Pricing in Ride-Sharing Networks,” Bimpikis, Candogan, and Saban explore the impact of the demand pattern for rides across a network’s locations on a platform’s optimal pricing and compensation policy, profits, and consumer surplus. They explicitly account for the pricing problem’s spatial dimension and the fact that the drivers endogenously determine whether and where to provide service. Their first contribution is to develop a tractable model to study a platform operating on a network of locations that may differ in both the size of their potential demand and the destination preferences of riders. Second, they provide a characterization of the platform’s optimal policy and identify “balancedness” of the demand pattern as a property that captures the profit potential of a given network. Finally, they discuss the benefits and limitations of a number of alternative pricing and compensation schemes. We explore spatial price discrimination in the context of a ride-sharing platform that serves a network of locations. Riders are heterogeneous in terms of their destination preferences and their willingness to pay for receiving service. Drivers decide whether and where to provide service so as to maximize their expected earnings given the platform’s pricing and compensation policy. Our findings highlight the impact of the demand pattern on the platform’s prices, profits, and the induced consumer surplus. In particular, we establish that profits and consumer surplus at the equilibrium corresponding to the platform’s optimal pricing and compensation policy are maximized when the demand pattern is “balanced” across the network’s locations. In addition, we show that they both increase monotonically with the balancedness of the demand pattern (as formalized by its structural properties). Furthermore, if the demand pattern is not balanced, the platform can benefit substantially from pricing rides differently depending on the location from which they originate. Finally, we consider a number of alternative pricing and compensation schemes that are commonly used in practice and explore their performance for the platform. The e-companion is available at https://doi.org/10.1287/opre.2018.1800 .

Crude Oil is one of the most important commodities in this world. We have studied the effects of Crude Oil inventories on crude oil prices over the last ten years (2011 to 2020). We tried to figure out how the Crude Oil price variance responds to inventory announcements. We then introduced several other financial instruments to study the relation of these instruments with Crude Oil variation. To undertake this task, we took the help of several mathematical tools including machine learning tools such as Long Short Term Memory(LSTM) methods, etc. The previous researches in this area primarily focussed on statistical methods such as GARCH (1,1) etc. (Bu (2014)). Various researches on the price of crude oil have been undertaken with the help of LSTM. But the variation of crude oil price has not yet been studied. In this research, we studied the variance of crude oil prices with the help of LSTM. This research will be beneficial for the options traders who would like to get benefit from the variance of the underlying instrument.

Investor stock market response has received a great deal of attention in the marketing literature. However, firms are not faceless corporations; individuals such as CEOs set their strategies. Upper echelon and strategic leadership theories hold that chosen strategies derive from these individuals’ opinions, which are a function of their personalities, demographics, experiences, and values. Building on recent literature, the authors propose how CEO characteristics can influence innovation and stock returns. Investors are motivated by cash flow expectations—in particular, the prospect of increasing and accelerating future cash flows, reducing associated risks, and increasing residual value. This systematic review focuses on four main characteristics—personality, demographics, experience and compensation—to arrive at a set of propositions on innovation and stock returns. After reviewing the extensive literature on CEO characteristics, the authors outline the emerging findings on CMO characteristics; propose future research directions on CEO and CMO characteristics, innovations, and stock returns; and offer implications for practice.

This paper investigates the leverage effect in African countries by applying normal and nonnormal distribution densities. Furthermore, we investigate the possible opportunities for portfolio diversification in South Africa, Nigeria, and Egypt. We find that negative stock returns do not generate higher volatility in further returns than past positive returns. All three countries are subject to the ARCH effect, where past stock information (volatility) influence the current stock returns (volatility). We also find that Gaussian distribution produces a better estimate as compared to non-normal distribution. In terms of portfolio diversification, returns are also subject to the ARCH effect, however, the leverage effect does not determine that past negative returns influence the current stock returns asymmetrically.

In recent years, online retailers (also called e-tailers) have started allowing manufacturers direct access to their customers while charging a fee for providing this access, a format commonly referred to as agency selling. In this paper, we use a stylized theoretical model to answer a key question that e-tailers are facing: When should they use an agency selling format instead of using the more conventional reselling format? We find that agency selling is more efficient than reselling and leads to lower retail prices; however, the e-tailers end up giving control over retail prices to the manufacturer. Therefore, the reaction by the manufacturer, who makes electronic channel pricing decisions based on their impact on demand in the traditional channel (brick-and-mortar retailing), is an important factor for e-tailers to consider. We find that when sales in the electronic channel lead to a negative effect on demand in the traditional channel, e-tailers prefer agency selling, whereas when sales in the electronic channel lead to substantial stimulation of demand in the traditional channel, e-tailers prefer reselling. This preference is mediated by competition between e-tailers—as competition between them increases, e-tailers prefer to use agency selling. We also find that when e-tailers benefit from positive externalities from the sales of the focal product (such as additional profits from sales of associated products), retail prices may be lower under reselling than under agency selling, and the e-tailers prefer reselling under some conditions for which they would prefer agency selling without the positive externalities. This paper was accepted by Chris Forman, information systems.