Dynamic Learning and Market Making in Spread Betting Markets with Informed Bettors

How Bookies Can Outwit Sophisticated Bettors Sports-betting markets are based entirely on predictions. A bettor has to pick a winning contestant, and a market maker―a bookie―bets on the opponent. As bookies have to take the other side of every bet, it is of great value to understand the market making problem, that is, how to set the spread lines as “prices” for the bookies. Nevertheless, understanding of this problem is limited. Specifically, sophisticated bettors exist in the market, and a bookie can be manipulated by skillful bettors because of information asymmetry. In “Dynamic Learning and Market Making in Spread Betting Markets with Informed Bettors,” Birge, Feng, Keskin, and Schultz study the market-making problem under information asymmetry and market manipulation. They show that, although many popular learning and pricing algorithms, such as Bayesian policies, are effective in learning, they are vulnerable to strategic manipulations. The authors propose a dynamic learning and pricing algorithm, called the inertial policy, that collects information from the market effectively but also protects the bookie from strategic manipulations.

Reinforcement Learning Approaches to Optimal Market Making

Market making is the process whereby a market participant, called a market maker, simultaneously and repeatedly posts limit orders on both sides of the limit order book of a security in order to both provide liquidity and generate profit. Optimal market making entails dynamic adjustment of bid and ask prices in response to the market maker’s current inventory level and market conditions with the goal of maximizing a risk-adjusted return measure. This problem is naturally framed as a Markov decision process, a discrete-time stochastic (inventory) control process. Reinforcement learning, a class of techniques based on learning from observations and used for solving Markov decision processes, lends itself particularly well to it. Recent years have seen a very strong uptick in the popularity of such techniques in the field, fueled in part by a series of successes of deep reinforcement learning in other domains. The primary goal of this paper is to provide a comprehensive and up-to-date overview of the current state-of-the-art applications of (deep) reinforcement learning focused on optimal market making. The analysis indicated that reinforcement learning techniques provide superior performance in terms of the risk-adjusted return over more standard market making strategies, typically derived from analytical models.

Halo Effect and Source Credibility in the Evaluation of Food Products Identified by Third-Party Certified Eco-Labels: Can Information Prevent Biased Inferences?

Despite the growing awareness of the need to promote the consumption of organic food, consumers have difficulties in correctly identifying it in the market, making frequent cognitive mistakes in the evaluation of products identified by sustainability labels and claims. This work analyzes the halo effect and the source credibility bias in the interpretation of product attributes based on third-party certified labels. It is hypothesized that, regardless of their specific meaning, official labels lead consumers to infer higher environmental sustainability, quality and price of the product, due to the credibility attributed to the certifying entity. It also examines the extent to which providing the consumer with accurate labeling information helps prevent biased heuristic thinking. An experimental between-subject study was performed with a sample of 412 Spanish business students and data were analyzed using partial least squares. Findings revealed that consumers tend to infer environmental superiority and, consequently, higher quality in products identified by both organic and non-organic certified labels, due to their credibility. Label credibility was also associated with price inferences, to a greater extent than the meaning attributed to the label. Interestingly, providing accurate information did not avoid biased heuristic thinking in product evaluation.

Bad Bank is Actually a Good Idea

This article demonstrates the effect of bad loans in India which was developed recently by the Indian Finance Minister. Bad banks operate like a concept in the domestic debt sector where the amount of domestic debt is high and even the market has enough scale to bear enough price-discovery and market-making. there was a proposal made to the government, to merge the NPA portfolio with a new establishment known as an Asset Rehabilitation Corporation (ARC), that would purchase the principal of the Non-performing Assets (NPA) portfolio at a book valuation (not market value) and these accounts would be taken over by the new company to manage the portfolio of the new command (which will be established with a capital of Rs 10,000 crore). The government says that it is intervening to mitigate potential damages that the banks could suffer as a result of the provisioning for non-performing assets and recapitalisation that the government (as a majority investor of most PSBs) may be required to invest on. With the forthcoming Union budget’s planning an outpouring of clamour and market demand is being felt to set up a ‘poor bank’ to sweep bad debts.