Polar Sun

QuoteAbstract

Financial markets were traditionally analyzed using the Efficient Market Hypothesis (EMH), which argued that asset prices fully reflected all available information. This led to the belief that price movements were random, making it impossible to consistently outperform the market. However, emerging research had highlighted that markets could exhibit multifractality, where inefficiencies existed across different time scales, challenging the traditional EMH framework. Our study built on these insights by investigating the market efficiency of several asset classes, including stock market indices, cryptocurrencies (Bitcoin, Ethereum, USDT, and Binance), commodities (gold and crude oil), fixed income (U.S. 10-Year & 30-Year Treasury Bonds), and foreign exchange (EUR/USD & GBP/USD). Utilizing the Multifractal Detrended Fluctuation Analysis (MFDFA) method, we analyzed the multifractal properties and efficiency of each asset class. The results revealed that traditional assets exhibited monofractality, adhering more closely to the EMH. In contrast, cryptocurrencies displayed significant inefficiencies, likely due to their volatility, market structure, and susceptibility to external shocks. The findings provided valuable insights into portfolio diversification and risk management, suggesting that inefficiencies in certain asset classes might present both opportunities and risks for investors.

QuoteIntroduction

The discipline of game theory was pioneered in the early 20th century by mathematicians Morgenstern's The breakthrough book, Ernst Theory came Zermelo with of games (1913) John and and von economic John Neumann von behavior, Neumann and published  (1928). Oscar in 1944. This was followed by important work by John Nash (1950-51) and Lloyd Shapley (1953). Game theory had a major influence on the development of several branches of economics (industrial organization, international trade, labor economics, macroeconomics, etc.). Over time the impact of game theory extended to other branches of the social sciences (political science, international relations, philosophy, sociology, anthropology, etc.) as well as to fields outside the social sciences, such as biology, computer science, logic, etc. In 1994 the Nobel prize in economics was given to three game theorists, John Nash, John Harsanyi and Reinhardt Selten, for their theoretical work in game theory which was very influential in economics. At the same time, the US Federal Communications Commission was using game theory to help it design a $7-billion auction of the radio spectrum for personal communication services (naturally, the bidders used game theory too!). The Nobel prize in economics was awarded to game theorists three more times: in 2006 to Robert Aumann and Thomas Schelling, in 2007 to Leonid Hurwicz, Eric Maskin and Roger Myerson and in 2010 to Lloyd Shapley and Alvin Roth.

Game theory provides a formal language for the representation and analysis of interactive situations, that is, situations where several "entities", called players, take actions that affect each other. The nature of the players varies depending on the context in which the game theoretic language is invoked: in evolutionary biology (see, for example, John Maynard Smith, 1982) players are non-thinking living organisms; in computer science (see, for example, Shoham-Leyton-Brown, 2008) players are artificial agents; in behavioral game theory (see, for example, Camerer, 2003) players are "ordinary" human beings, etc. Traditionally, however, game theory has focused on interaction among intelligent, sophisticated and rational individuals. For example, Aumann describes game theory as follows:

"Briefly put, game and economic theory are concerned with the interactive behavior of Homo rationalis - rational man. Homo rationalis is the species that always acts both purposefully and logically, has well-defined goals, is motivated solely by the desire to approach these goals as closely as possible, and has the calculating ability required to do so." (Aumann, 1985, p. 35.)

This book is concerned with the traditional interpretation of game theory.

Game theory is divided into two main branches. The first is cooperative game theory, which assumes that the players can communicate, form coalitions and sign binding agreements. Cooperative game theory has been used, for example, to analyze voting behavior and other issues in political science and related fields. We will deal exclusively with the other main branch, namely non-cooperative game theory. Non-cooperative game theory models situations where the players are either unable to communicate or are able to communicate but cannot sign binding contracts. An example of the latter situation is the interaction among firms in an industry in an environment where antitrust laws make it illegal for firms to reach agreements concerning prices or production quotas or other forms of collusive behavior.

QuoteThe Federal Reserve System is the central bank of the United States. It performs five general functions to promote the effective operation of the U.S. economy and, more generally, the public interest. The Federal Reserve
 

• conducts the nation's monetary policy to promote maximum employment and stable prices in the U.S. economy;
• promotes the stability of the financial system and seeks to minimize and contain systemic risks through active monitoring and engagement in the U.S. and abroad;
• promotes the safety and soundness of individual financial institutions and moniors their impact on the financial system as a whole;
• fosters payment and settlement system safety and efficiency through services to the banking industry and the U.S. government that facilitate U.S.-dollar transactions and payments; and
• promotes consumer protection and community development through consumer-focused supervision and examination, research and analysis of emerging consumer issues and trends, community economic development activities, and the administration of consumer laws and regulations.

QuoteCrowd Behavior in Prediction, Betting, and Stock Markets

Introduction

In 1932, Bernard Baruch, a wealthy financier who made his fortune on Wall Street in the early 20th century, contributed the foreword to a reprint of the 1852 edition of Charles Mackay's classic book on markets, Memoirs of Extraordinary Popular Delusions and the Madness of Crowds.

Invoking a dictum from Friedrich von Schiller, a German poet and philosopher, Baruch wrote: "Anyone taken as an individual, is tolerably sensible and reasonable—as a member of a crowd, he at once becomes a blockhead." He added, "Without due recognition of crowd-thinking (which often seems crowd-madness) our theories of economics leave much to be desired."

About 40 years later, Eugene Fama, a professor of finance at the University of Chicago and a winner of the Nobel Prize in Economics, published "Efficient Capital Markets: A Review of Theory and Empirical Work." It is among the most famous papers ever written in finance. This might be considered the theory Baruch had in mind. Fama posited, "A market in which prices always 'fully reflect' available information is called 'efficient.'"

Fama found that strategies investors commonly applied to try to outperform the market, including using past price patterns to project the future and doing fundamental analysis to distinguish between price and value, failed in their objective. In other words, there is no reliable way to take advantage of the blockheads.

Nearly all those who study markets carefully agree that they appear sensible for the most part, as theory would have it, but periodically go bonkers.3 Having one framework to accommodate both realities is useful.

James Surowiecki wrote about such an approach in 2004.4 Riffing on Mackay's madness of crowds, Surowiecki called his book, The Wisdom of Crowds. He showed that crowds can be remarkably accurate in reflecting objective values or outcomes. Indeed, the prices generated by collectives commonly converge on the proper theoretical price in experimental settings. This thinking runs against the idea that individuals are reasonable and crowds mad.

But key to Surowiecki's case is that the wisdom of crowds depends on satisfying certain conditions. When those conditions are in effect, crowds are generally wise. When one or more of those conditions are violated, crowds can be mad.

This report specifies those conditions, identifies various types of problems, and examines how they apply to prediction markets, sports betting, parimutuel betting, and the stock market. The goal is to see how the conditions function in each market, and where they are the same or different for each.

These concepts are useful for investors for a few reasons. First, the wisdom (and madness) of crowds is a sound way to explain market behavior. Practitioners have had a sense of this for centuries and academics now take the idea seriously.6 Second, prediction markets provide real-time probabilities for events that may be helpful to investors.

Finally, the conditions for collective intelligence apply within organizations as well, which has relevance for hiring and training employees as well as structuring meetings and making decisions. There remains a large gap between what research reveals as best practices and what most organizations actually do.