Ticker Symbol Research Articles

Validity Testing of Classical Asset Allocation Models: An Empirical Study

This paper uses a 20 years of daily total return data for the S&P 500 Index (ticker symbol SPX) and 10 stocks, make all the necessary calculations to plot a portfolio area that gather an efficient boundary, a minimum risk or variance boundary, and a minimum return boundary together for a given set of constraints. Analyze all the outcomes in order to compare the various restrictions for each optimization issue (MM and IM), as well as the two solutions to the same optimization problem. The Excel solver was the primary tool utilized during calculation to resolve optimization issues for each point on the minimal risk or variance border. Also, this paper use an Excel solution table to calculate a large number of multipoints on any desired boundary. Through calculation and research, we found that, Markowitz model makes full use of covariance matrix to generate excellent portfolio. However, the results are numerically unstable. At the same time, the hypothesis of normality, stationarity and mean square error are verified. The exponential model simplifies the Markowitz model and produces more robust results. However, it introduces additional assumptions about the independence, normality, and homoscedasticity of the regression residuals, which are also invalid. The reduction (CDaR) model is hypothesis-free. The numerical stability can be obtained by transforming the nonlinear optimization problem into a linear programming problem.

The supply of information and price formation: Evidence from Google's search engine

AbstractThis study develops several Google search‐based measures to test the relation between earnings‐week online search results and the speed of price discovery. These measures are based on searches using only a firm's ticker symbol in the search string. I collect the total number of search results (across all search result pages) as well as the type and content of search results on the first three pages of search results. I find that the quantity, quality, and content of search results have varying effects on the speed at which earnings news is impounded into stock price. I also find that effects are only observed for search results presented on the first page of a Google search. Overall, my results suggest that (1) increases in online information are associated with slower price discovery, and (2) the likely mechanism by which this association operates is through useful search results being crowded off the first page of results by more complex or irrelevant search results.

Market Reaction to Local Attention around Earnings Announcements in China: Evidence from Internet Search Activity

The existing literature shows that, due to locality and familiarity, spatial investor–firm adjacency plays a key role in determining stock investor attention, as proxied by the location where investors initiate an Internet search of the ticker symbol. This paper investigates whether Chinese stock markets exhibit the same pattern observed in the U.S. market—demand for a firm’s information can exert strong effects on the stock market response during earnings announcements of the firm. Specifically, for each Chinese publicly listed firm, this paper constructs a dynamic local attention ratio that divides the search volume generated within the province where the firm is registered by the contemporaneous nationwide search volume consisting of 31 provinces in total. During 2011–2018 in China’s A-share market, it was found that the local attention measure leads the market response around annual earnings announcement dates. For firms with higher local attention, trading is more active before the announcement, and the price changes can better predict incoming earnings news. At the announcement, while trading remains active, the predictivity of price changes for earnings becomes weaker. Furthermore, we only found evidence that supports the local information advantage argument in explaining the locality of Chinese investors’ attention.

Notable moments in 2021

PHARMACEUTICALS Notable moments in 2021The drug industry remained focused on conquering COVID-19, but the year also brought advances—and setbacks—in other areas. Here are a few highlights from the year in pharma ShareShare onFacebookTwitterWechatLinked InRedditEmail C&EN, 2021, 99 (44), p 42December 6, 2021Cite this:C&EN 99, 44, 42COVID-19 vaccines from Moderna, Johnson & Johnson, and Pfizer and BioNTech were rolled out across the US. According to the Centers for Disease Control and Prevention, about 70.2% of the US population had received at least one dose of a COVID-19 vaccine as of Nov. 30. (Credit: Ringo Chiu/Shutterstock)Figure1of6On May 28, the US Food and Drug Administration approved Amgen’s Lumakras to treat lung cancers driven by KRAS G12C mutations. The drug is the first to inhibit KRAS, a common cancer driver that is notoriously difficult to overcome.Figure1of6Frances Collins (Credit: National Institutes of Health)Figure2of6On Oct. 6, the World Health Organization recommended broad use of the malaria vaccine RTS,S. The four-dose regimen does not provide complete protection but could significantly reduce the roughly 400,000 annual deaths caused by malaria. (Credit: Getty Images)Figure3of6On June 4, the FDA approved Novo Nordisk’s Wegovy, a glucagon-like peptide 1 mimic that is the first new weight-loss management treatment since 2014. (Credit: Novo Nordisk)Figure4of6Ending months of speculation, President Joe Biden nominated Robert Califf as FDA commissioner on Nov. 12. Califf served as commissioner in the Barack Obama administration. (Credit: US Food and Drug Administration)Figure5of6July 7: Date when the contagious Delta variant officially became the dominant form of the coronavirus in the US, according to the CDC “I am proud of all we’ve accomplished. I fundamentally believe, however, that no single person should serve in the position too long, and that it’s time to bring in a new scientist to lead the NIH into the future.”—Francis Collins, in an Oct. 5 statement announcing his decision to step down as director of the US National Institutes of Health at the end of 2021$11.5 billion: Amount Merck & Co. agreed to pay to acquire Acceleron Pharma, in the industry’s largest deal to date in 2021>5.2 million: Number of deaths from COVID-19 worldwide as of Dec. 1On Jan. 20, the FDA approved ViiV Healthcare’s Cabenuva, the first long-acting HIV therapy. The once-monthly injection is a combination of ViiV’s cabotegravir and Johnson & Johnson’s rilpivirine.$56,000: Annual cost of Biogen’s controversial Alzheimer’s drug, Aduhelm, which the FDA approved on June 7“The HIV vaccine story is a story of great disappointment. I think that the vaccine is the holy grail, but we haven’t found it yet.”—Warner Greene, director, Gladstone Institutes’ Michael Hulton Center for HIV Cure Research, on news that Johnson & Johnson’s HIV vaccine had failed in a Phase 2 trial in Africa$20 billion: Valuation of synthetic biology company Ginkgo Bioworks when it began trading on the New York Stock Exchange on Sept. 17 under the ticker symbol “DNA”

Kinetic model of wealth distribution by trading stocks with geometric brownian motion

In this paper, we consider the wealth distribution obtained by trading (buying–selling) stocks whose prices follow the geometric Brownian motion (GBM), when both number of the ticker symbol of the stock and maximum number of the traded stock are limited to unity. The binary exchange of the cash and stock between two agents is expressed with the Boltzmann-type kinetic equation. The distribution function of the number of the agents with the specific number of the stock or specific amount of the cash can be demonstrated, theoretically, when the price of the stock is constant. The distribution function of the number of the agents with the specific amount of the total asset can be approximated by [Formula: see text]-distribution, when the price of the stock follows the GBM. Finally, the rule in the binary-exchange-game approximates the distribution function of the number of the agents with the specific amount of the total asset to the Feller–Pareto-like distribution at the high wealth tail.

Q2 2021 Silicon Valley Venture Capitalist Confidence Index Research Report

The Silicon Valley Venture Capitalist Confidence IndexTM (Bloomberg ticker symbol: SVVCCI) is based on a recurring quarterly survey (since Q1 2004) of Silicon Valley/San Francisco Bay Area venture capitalists. The Index measures and reports the opinions of professional venture capitalists on their estimations of the high-growth venture entrepreneurial environment in the San Francisco Bay Area over the next 6 - 18 months. The Silicon Valley Venture Capitalist Confidence IndexTM for the second quarter of 2021, based on a June 2021 survey of 32 San Francisco Bay Area venture capitalists, registered 4.16 on a 5-point scale (with 5 indicating high confidence and 1 indicating low confidence). The Q2 2021 Index surged higher from the prior quarter’s reading of 3.82 and highlights the continuing strong recovery in the entrepreneurial economy since the pandemic low point confidence measure of 2.33 in the first quarter of 2020.

Short-term volatility forecasting with kernel support vector regression and Markov switching multifractal model

In volatility forecasting literature, Markov switching multifractal (MSM) models are well known for capturing many important stylized facts such as long memory and fat tails. MSM delivers stronger performance both in- and out-of-sample than GARCH-type models in long-term forecasts. However, the literature shows that MSM forecasts only slightly improve on GARCH(1,1) at short-term intervals. This indicates that there may exist certain patterns to be discovered in the innovation part . To enhance MSM's prediction accuracy at the short-term level with higher frequency data, a hybrid model of the MSM model and support vector regression (SVR) is proposed, in which a particle swarm optimization (PSO) algorithm is applied to optimize hyperparameters of the support vector regression in the scope of constraint permission. The method is referred to as MSM-PSO-SVR. Further, we introduce the Fourier kernel MSM-PSO-SVR and evaluate the performance of various MSM-PSO-SVR models in terms of mean absolute error (MAE) and the mean squared error (MSE) with one-minute data of the exchange traded fund (ETF) SPDR S&P 500 Trust ETF (ticker symbol: SPY). The experimental results show that the proposed approach outperforms the other competing peer models and in particular, the selection of SVR kernel might yield significant boosts in forecasting ability. Results of Hansen's Superior Predictive Ability test further validate the conclusion.

Danimer goes public

The biobased polymer maker Danimer Scientific has completed its merger with Live Oak Acquisition, a “blank check” company created to make a large purchase. Danimer now trades on the New York Stock Exchange under the ticker symbol DNMR. The firm has $380 million in cash, which it plans to use mainly to increase its capacity to make the biodegradable polymer polyhydroxyalkanoate to 90,000 metric tons (t) per year by 2023 from 10,000 t today. Danimer had sales of $35 million in the first 9 months of 2020.

The VIX Index Under Scrutiny of Machine Learning Techniques and Neural Networks

The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the market’s expected volatility on the S&P 500 Index, calculated and published by the Chicago Board Options Exchange (CBOE). It is also often referred to as the fear index or the fear gauge. The current VIX index value quotes the expected annualized change in the S&P 500 index over the following 30 days, based on options-based theory and current options-market data. Despite its theoretical foundation in option price theory, CBOE’s Volatility Index is prone to inadvertent and deliberate errors because it is weighted average of out-of-the-money calls and puts which could be illiquid. Many claims of market manipulation have been brought up against VIX in recent years. This paper discusses several approaches to replicate the VIX index as well as VIX futures by using a subset of relevant options as well as neural networks that are trained to automatically learn the underlying formula. Using subset selection approaches on top of the original CBOE methodology, as well as building machine learning and neural network models including Random Forests, Support Vector Machines, feed-forward neural networks, and long short-term memory (LSTM) models, we will show that a small number of options is sufficient to replicate the VIX index. Once we are able to actually replicate the VIX using a small number of S&P options we will be able to exploit potential arbitrage opportunities between the VIX index and its underlying derivatives. The results are supposed to help investors to better understand the options market, and more importantly, to give guidance to the US regulators and CBOE that have been investigating those manipulation claims for several years.

Silicon Valley Venture Capitalist Confidence IndexTM Research Report Q3 2020

The Silicon Valley Venture Capitalist Confidence IndexTM (Bloomberg ticker symbol: SVVCCI) is based on a recurring quarterly survey (since Q1 2004) of Silicon Valley/San Francisco Bay Area venture capitalists. The Index measures and reports the opinions of professional venture capitalists on their estimations of the high-growth venture entrepreneurial environment in the San Francisco Bay Area over the next 6 - 18 months. The Silicon Valley Venture Capitalist Confidence IndexTM for the third quarter of 2020, based on a September 2020 survey of 31 San Francisco Bay Area venture capitalists, registered 3.39 on a 5-point scale (with 5 indicating high confidence and 1 indicating low confidence). This quarter’s Index measurement rose for the second consecutive quarter from the all-time low reading of 2.33 in the first quarter of 2020. Please see Graph 1 for quarterly trend data.

The Mechanisms of Derivatives Market Efficiency

These are not your parents’ financial markets. A generation ago, the image of Wall Street was one of floor traders and stock brokers, of opening bells and ticker symbols, of titans of industry and barbarians at the gate. These images reflected the prevailing view in which stock markets stood at the center of the financial universe. The high point of this equity-centric view coincided with the development of a significant body of empirical literature examining the efficient market hypothesis (EMH): the prediction that prices within an efficient stock market will fully incorporate all available information. Over time, this equity-centric view became conflated with these empirical findings, transforming the EMH in the eyes of many observers from a testable prediction about how rapidly new information is incorporated into stock prices into a more general – and generally unexamined – statement about the efficiency of financial markets.In their seminal 1984 article The Mechanisms of Market Efficiency, Ron Gilson and Reinier Kraakman advanced a causal framework for understanding how new information becomes incorporated into stock prices. Gilson and Kraakman’s framework provided an institutional explanation for the empirical findings supporting the EMH. It has also played an influential role in public policy debates surrounding securities fraud litigation, mandatory disclosure requirements, and insider trading restrictions. Yet despite its enduring influence, there have been few serious attempts to extend Gilson and Kraakman’s framework beyond the relatively narrow confines in which it was originally developed: the highly regulated, order-driven, and extremely liquid markets for publicly-traded stocks. This article examines the mechanisms of derivatives market efficiency. These mechanisms respond to information and other problems not generally encountered within conventional stock markets. These problems reflect important differences in the nature of derivatives contracts, the structure of the markets in which they trade, and the sources of market liquidity. Predictably, these problems have led to the emergence of very different mechanisms of market efficiency. This article describes these problems and evaluates the likely effectiveness of the mechanisms of derivatives market efficiency. It then explores the implications of this evaluation in terms of the current policy debates around derivatives trade reporting and disclosure, the macroprudential surveillance of derivatives markets, the push toward mandatory central clearing of derivatives, the prudential regulation of derivatives dealers, and the optimal balance between public and private ordering.

The name game: The importance of resourcefulness, ruses, and recall in stock ticker symbols

A BABY, a GEEK, and a COW all walk into a bar looking for some BEER and VINO. What happens next? They all beat the market. Head, Smith, and Wilson (2009) (the “2009 Study”) found that a portfolio of stocks with clever ticker symbols beat the market by a substantial margin during the years 1984–2005.In recent years, the credibility of scientific research has been damaged by the reproducibility crisis—the failure of attempts to replicate published, peer-review studies. Two important examples are Herndon, Ash, and Pollin (2014) and Shanks et al. (2013).We re-examine the 2009 study’s surprising conclusion by updating the analysis for the subsequent years 2006 through 2018. In addition, we replicate its methodology with a new list of NASDAQ clever-ticker stocks.

Silicon Valley Venture Capitalist Confidence Index™ Q1 2019

The Silicon Valley Venture Capitalist Confidence Index™ (Bloomberg ticker symbol: SVVCCI) is based on a recurring quarterly survey (since Q1 2004) of Silicon Valley/San Francisco Bay Area venture capitalists. The Index measures and reports the opinions of professional venture capitalists on their estimations of the high-growth venture entrepreneurial environment in the San Francisco Bay Area over the next 6 - 18 months. The Silicon Valley Venture Capitalist Confidence Index™ for the first quarter of 2019, based on a March 2019 survey of 26 San Francisco Bay Area venture capitalists, registered 3.63 on a 5-point scale (with 5 indicating high confidence and 1 indicating low confidence). This quarter’s Index measurement climbed significantly from the previous quarter’s Index reading of 3.20 which was the lowest level since Q1 2009.

Predicting the daily return direction of the stock market using hybrid machine learning algorithms

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF (ticker symbol: SPY) based on 60 financial and economic features. DNNs and traditional artificial neural networks (ANNs) are then deployed over the entire preprocessed but untransformed dataset, along with two datasets transformed via principal component analysis (PCA), to predict the daily direction of future stock market index returns. While controlling for overfitting, a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000. Moreover, a set of hypothesis testing procedures are implemented on the classification, and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset, as well as several other hybrid machine learning algorithms. In addition, the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested, including in a comparison against two standard benchmarks.

Corteva and DuPont split from DowDuPont

The DowDuPont era has come to an end. Corteva Agriscience has separated from DowDuPont, and the remaining entity is being recast as DuPont. Corteva combines the crop protection chemical and seed businesses of Dow Chemical and DuPont, which merged in 2017 to form DowDuPont. The businesses that make up Corteva had sales of $14.3 billion in 2018. Some 56% of these sales came from seeds; the balance was derived from crop protection chemicals. DowDuPont shareholders received one share of Corteva for every three shares of DowDuPont they owned. It began trading on the New York Stock Exchange on June 3 under the ticker symbol CTVA. “Today marks the launch of a new kind of agriculture company, well positioned to compete and win by providing farmers the complete solution they need for sustainable, long-term growth and improved profitability,” said Corteva CEO Jim Collins, marking the occasion. DuPont has four business segments

Dow is on its own again

Dow completed its separation from DowDuPont on April 1. DowDuPont shareholders received one share of Dow for every three they own of DowDuPont. The company began trading on the New York Stock Exchange under its old ticker symbol, DOW, and closed at $53.50 per share on April 2. “The new Dow is a more focused and streamlined company with a clear playbook to deliver long-term earnings growth and value creation,” CEO Jim Fitterling says. DowDuPont is scheduled to further split into DuPont and Corteva Agriscience on June 1. The new company is beginning life in turbulent waters. DowDuPont announced that sales for its materials science division, which turned into the new Dow, are expected to decline in the “low-teens” percent in the first quarter versus the year-ago period. Pretax profits will decline more than 20%. Meanwhile, Corteva management also expects to see a sales decline owing to midwestern flooding.

A methodology for the resolution of cashtag collisions on Twitter – A natural language processing & data fusion approach

Investors utilise social media such as Twitter as a means of sharing news surrounding financials stocks listed on international stock exchanges. Company ticker symbols are used to uniquely identify companies listed on stock exchanges and can be embedded within tweets to create clickable hyperlinks referred to as cashtags, allowing investors to associate their tweets with specific companies. The main limitation is that identical ticker symbols are present on exchanges all over the world, and when searching for such cashtags on Twitter, a stream of tweets is returned which match any company in which the cashtag refers to - we refer to this as a cashtag collision. The presence of colliding cashtags could sow confusion for investors seeking news regarding a specific company. A resolution to this issue would benefit investors who rely on the speediness of tweets for financial information, saving them precious time. We propose a methodology to resolve this problem which combines Natural Language Processing and Data Fusion to construct company-specific corpora to aid in the detection and resolution of colliding cashtags, so that tweets can be classified as being related to a specific stock exchange or not. Supervised machine learning classifiers are trained twice on each tweet – once on a count vectorisation of the tweet text, and again with the assistance of features contained in the company-specific corpora. We validate the cashtag collision methodology by carrying out an experiment involving companies listed on the London Stock Exchange. Results show that several machine learning classifiers benefit from the use of the custom corpora, yielding higher classification accuracy in the prediction and resolution of colliding cashtags.

The VIX Volatility Index - A Very Thorough Look at It

The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the stock market’s expectation of volatility implied by S&P 500 index options, calculated and published by the Chicago Board Options Exchange (CBOE). It is colloquially referred to as the fear index or the fear gauge. The current VIX index value quotes the expected annualized change in the S&P 500 index over the following 30 days, as computed from options-based theory and current options-market data. Despite its theoretical foundation in option price theory, CBOE’s Volatility Index is prone to inadvertent and deliberate errors. We shed light on many claims that have been brought up against the VIX in recent years. Replicating the VIX index by using empirical options data from CBOE we show which one of those claims are justified and lead to meaningful alterations of the VIX. Three main areas can be identified: Eight different alteration possibilities of the current VIX formula, three deficiencies in the theoretical derivation of the VIX and two major issues with the market microstructure of the VIX and its derivatives. First, eight theoretically possible ways of altering the current VIX formula are identified. We will thoroughly investigate those ways both from a theoretical point of view and using empirical data. The two major alteration possibilities are identified and their potential impact on the VIX calculation is shown using actual option quote data from the CBOE. Second, the theoretical derivation behind the VIX formula has three major flaws. We show their importance based on an empirical analysis of the VIX and the underlying options on the S&P 500. Our analysis helps to understand and put into context previous claims that were brought up against the VIX. Third, the market microstructure behind the pricing of VIX derivatives appears to be flawed. Very liquid derivatives are priced off an illiquid auction mechanism of out-of-the-money put and call options. To make matters worse, those derivatives are cash-settled and the settlement value of the VIX, as used for the pricing of its derivatives, is based on a different calculation than the regular VIX index. Finally, we also investigate claims that the settlement values can substantially deviate from the previous-day close. We show that those claims are based on a wide-spread misunderstanding of how the settlement values and the VIX index are calculated and that they are not justified in the way that they are brought up at the moment. Our empirical methodology is based on replicating the VIX based on the underlying quote data. Our conclusion is that the VIX needs a thorough review of both its theoretical foundations, its calculations and the market mechanism behind computing the value of its derivatives. Furthermore, some of the claims brought up against the VIX are more of a theoretical nature and not important, but some might have a dramatic impact on the value of the VIX as well as the pricing of its derivatives. To our knowledge, that is the first comprehensive study of the VIX index and its shortcomings that is both based on theoretical and empirical observations. We are confident that our analysis will lead to new ways of how to make the VIX index safer for investors and market participants as well as less prone to manipulation and errors.

The value of publicly available predicted earnings surprises

This paper demonstrates how to collect and manage free predicted earnings surprises available in the public domain. The predicted earnings surprises we collect are expected to be more accurate than the corresponding consensus estimates and other predicted earnings, but have not been studied in the academic literature until very recently. We find a number of unexpected and problematic idiosyncrasies with the source of the data and the predicted earnings surprises themselves. The data are hard to work with, perhaps by design, and contain both big and small extreme values that are unexpected given their origin. It is unclear how these observations are selected for public release. After the data science exercise of managing and merging the predicted earnings surprises with other freely available public information (specifically ticker symbols and return data), we examine the predicted earnings surprises and investigate how the predicted earnings surprises affect short-term stock prices. We find evidence of a linear association between the predicted earnings surprises and subsequent short-term returns, although the significance is driven by extreme outliers. Most importantly, we use the predicted earnings surprises to form short-term trading strategies. The most profitable trading strategy that exploits the predicted earnings surprises is a contrarian trading strategy.

Silicon Valley Venture Capitalist Confidence Index Q1 2018

The Silicon Valley Venture Capitalist Confidence Index™ (Bloomberg ticker symbol: SVVCCI) is based on a recurring quarterly survey of San Francisco Bay Area/Silicon Valley venture capitalists. The Index measures and reports the opinions of professional venture capitalists on their estimations of the high growth venture entrepreneurial environment in the San Francisco Bay Area over the next 6 - 18 months. The Silicon Valley Venture Capitalist Confidence Index™ for the first quarter of 2018, based on a March 2018 survey of 34 San Francisco Bay Area venture capitalists, registered 3.76 on a 5-point scale (with 5 indicating high confidence and 1 indicating low confidence). This quarter’s index measurement increased from the previous quarter’s index reading of 3.66; and also rose above its 14-year average of 3.71. The increasing sentiment was driven in large measure by expectations of an improving exit market, abundant capital, and a continuing wave of sophisticated new ventures that aim to transform business as we know it.