Nasdaq (United States)

We investigate how high-frequency trading (HFT) in equity markets affects options market liquidity. We find that increased aggressive HFT activity in the stock market leads to wider bid–ask spreads in the options market through two main channels. First, options market makers’ quotes are exposed to sniping risk from HFTs exploiting put–call parity violations. Second, informed trading in the options market further amplifies the impact of HFT in equity markets on the liquidity of options by simultaneously increasing the options bid–ask spread and intensifying aggressive HFT activity in the underlying market.

The majority of multiasset investment portfolios allocate most of their assets to a mix of stocks and bonds, ostensibly relying on the observed negative correlation between the two asset classes for diversification. Unfortunately, the observed negative correlation between equities and fixed income is not guaranteed to continue. Furthermore, although rates are beginning to rise from historic lows, bonds may take some time to provide satisfactory yields. Equity options may be used to reduce the risk of portfolios, generate income, or both. Historically, options strategies have been out of reach for most investors, but a growing number of exchange-traded funds offer access to option strategy indexes that can provide the potential for material improvements in the risk and reward of portfolios. In this article, the authors introduce a set of option strategy indexes linked to the NASDAQ-100 Index that can be accessed via exchange-traded funds and demonstrate how they can add value to multiasset portfolios.

Machine learning and artificial intelligence have evolved beyond simple hype and have integrated themselves in business and in popular conversation as an increasing number of smart applications profoundly transform the way we work and live. This article defines machine learning in terms of potential benefits and pitfalls for a nontechnical audience, and gives examples of popular and powerful machine learning algorithms: k-means clustering, principal component analysis, and artificial neural networks. Three important philosophical challenges of machine learning are introduced: the no free lunch theorem, the curse of dimensionality, and the bias–variance trade-off.

In this paper, an Artificial Neural Network study has been implemented to forecast the prediction of precious metals such as gold, silver, platinum and palladium prices by using RapidMiner data mining software. The five performance measures; root mean squared error, absolute error, relative error, Spearman's Rho and Kendall’s Tau are utilized to evaluate artificial neural network model. This study concentrates on data which includes gold, silver, palladium, platinum, Brent Petrol, natural gas prices, 30 years’ bond, 10 years’ bond, 5 years’ bond, S&P 500, Nasdaq, Dow Jones, FTSE100, DAX, CAC40, SMI, NIKKEI, HANH, SENG and Euro/USD within the period of 4th of January 2010 to 14th of December 2015. The prices on the last quarter of 2015 is used for forecasting and validation. The results show that error rates are accurate in order to foresee the market trends.

It is with great pleasure I introduce my professional colleague and long-time friend, Frank Hatheway. In doing so, I would like to share a story with you about Frank. As many of you already know, I have been a proponent of call auctions for many years. In my efforts to build support, I had talked to Frank and NASDAQ in the past about introducing a call auction at NASDAQ. I did not meet with a great deal of success. Well, lo and behold, one day I was in my office at Baruch at about 5:30 in the afternoon when my phone rang. “Hello, Bob, it’s Frank,” the voice on the other end said. “We are doing it Bob. We are putting in a call!”

Abstract The banking and finance sector requires secure, resilient, and reliable systems to ensure seamless operations and maintain public confidence in monetary systems. Many financial institutions are at the forefront of developing best practices and deploying advanced technologies to secure their systems and assets. More importantly, the sector serves as a model of the cooperation necessary to develop standards and best practices that benefit all sectors. This article identifies some of the important cyber security‐related collaboration among the private and public sector organizations that make up the sector, and explores the culture that enables such cooperation. Information security is an important concern for all institutions in the banking and finance sector. Financial institutions are persistently targeted by criminals and others with malicious intent. To address these problems, institutions across the sector worked collaboratively to improve inter‐ and intrasector communication and created private–public partnerships for information sharing and encouraging innovation. This article explores the foundations of cooperation and information sharing, including some of the major initiatives, associations, and challenges the sector faced as it worked to improve defenses against attack, enhance resiliency, and sustain public confidence in trusted banking relationships.