Research

Michael I. Jordan has written a thought-provoking article on the paradox of progress in Artificial Intelligence (AI). We are surrounded by previously unthinkable advances in this field—facial recognition, natural language translation, voice recognition and generation, autonomous vehicles, and the casual mastery of complex games such as chess and Go—and new breakthroughs seem to emerge almost daily. On the other hand, few of these advances have felt like a true revolution, and when they have, such as the ability to exploit social networks for targeted advertising and behavioral modification, it has often felt like a step backward from a societal perspective. Jordan makes a strong case for the necessity of a new field of human-centered engineering for systems within the several fields now comprising AI research.

Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406,038 entries of clinical trial data for over 21,143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.

Capitalism is a powerful tool: By compressing enormous amounts of information regarding supply and demand into a single number—the market price—buyers and sellers are able to make remarkably intelligent decisions simply by engaging in self-interested behavior. But in a big-data world, where a supercomputer can fit into our pocket and a simple Internet search allows us to find every product under the Sun, do we still need it?

In Reinventing Capitalism in the Age of Big Data, Viktor Mayer-Schönberger and Thomas Ramge argue that big data will transform our economies on a fundamental level. Money will become obsolete, they argue, replaced by metadata. Instead of a single market price for each commodity, sophisticated matching algorithms will use a bundle of specifications and personal preferences to select just the right product for you. Artificial intelligence powered by machine-learning techniques will relentlessly negotiate the best possible transaction on your behalf. Capital will still be important, they concede, but increasingly just for its signaling content. “Venture informers” might even replace venture capitalists.

‘Fintech’ is transforming the financial sector at a pace that is now obvious even to the casual observer. We see this not only in daily headlines about initial coin offerings or financial applications of blockchain technology, but also in the daily experiences of the average consumer: paper cheques consigned forever to desk drawers, automatic currency conversions on a trip abroad, the rapid approval of an online loan – and most excitingly for some, personal investing.

The increasing dominance of technology in daily lives is finally penetrating the financial industry as well. The growing popularity of algorithmic trading, mobile payment platforms and robo-advisers is just the beginning of the fintech revolution. But perhaps the most radical - and controversial - innovation in today's headlines is cryptocurrencies. Extreme volatility makes products an unreliable store of value - for now.

In this article, the authors analyze the relation between stock market liquidity and real-time measures of sentiment obtained from the social-media platforms StockTwits and Twitter. The authors find that extreme sentiment corresponds to higher demand for and lower supply of liquidity, with negative sentiment having a much larger effect on demand and supply than positive sentiment. Their intraday event study shows that booms and panics end when bullish and bearish sentiment reach extreme levels, respectively. After extreme sentiment, prices become more mean-reverting and spreads narrow. To quantify the magnitudes of these effects, the authors conduct a historical simulation of a market-neutral mean-reversion strategy that uses social-media information to determine its portfolio allocations. These results suggest that the demand for and supply of liquidity are influenced by investor sentiment and that market makers who can keep their transaction costs to a minimum are able to profit by using extreme bullish and bearish emotions in social media as a real-time barometer for the end of momentum and a return to mean reversion.

Breakthroughs in computing hardware, software, telecommunications, and data analytics have transformed the financial industry, enabling a host of new products and services such as automated trading algorithms, crypto-currencies, mobile banking, crowdfunding, and robo-advisors. However, the unintended consequences of technology-leveraged finance include firesales, flash crashes, botched initial public offerings, cybersecurity breaches, catastrophic algorithmic trading errors, and a technological arms race that has created new winners, losers, and systemic risk in the financial ecosystem. These challenges are an unavoidable aspect of the growing importance of finance in an increasingly digital society. Rather than fighting this trend or forswearing technology, the ultimate solution is to develop more robust technology capable of adapting to the foibles in human behavior so users can employ these tools safely, effectively, and effortlessly. Examples of such technology are provided.

With the rise of social media, investors have a new tool for measuring sentiment in real time. However, the nature of these data sources raises serious questions about its quality. Because anyone on social media can participate in a conversation about markets—whether the individual is informed or not—these data may have very little information about future asset prices. In this article, the authors show that this is not the case. They analyze a recurring event that has a high impact on asset prices—Federal Open Market Committee (FOMC) meetings—and exploit a new dataset of tweets referencing the Federal Reserve. The authors show that the content of tweets can be used to predict future returns, even after controlling for common asset pricing factors. To gauge the economic magnitude of these predictions, the authors construct a simple hypothetical trading strategy based on this data. They find that a tweet-based asset allocation strategy outperforms several benchmarks—including a strategy that buys and holds a market index, as well as a comparable dynamic asset allocation strategy that does not use Twitter information.

Moderator Martin Leibowitz asked a panel of industry experts—Andrew W. Lo, Robert C. Merton, Stephen A. Ross, and Jeremy Siegel—what they saw as the most important issues in finance, especially as those issues relate to practitioners. Drawing on their vast knowledge, these panelists addressed topics such as regulation, technology, and financing society’s challenges; opacity and trust; the social value of finance; and future expected returns.

WSJ Wealth Expert Andrew W. Lo of MIT says robo advisers are the rotary phones to today’s iPhone--technology that has great potential but it still immature.