Literature Review: Predicting the Stock Market Using Machine Learning and Sentiment Analysis

Result #1

Literature Review: Predicting the Stock Market Using Machine Learning and Sentiment Analysis in Personal Experiments With Style (A Review), 2007. This shows only one example, which ran in the December 2005 issue of the Journal of Experimental Psychology. For the remainder of this series, a collection of fifty-six pieces, Rabinowitz became increasingly self-conscious about the use of specific types of information, which she (and, ultimately, readers) rejected, and began to combine information with other data to arrive at a self-aware practice. The emphasis of the work, from the beginning, was on the mental process that the artist uses to arrive at her works meaning. In other words, the artist has no control over her interpretation of the data. The situation, as an artist, Rabinowitz may not always like it, but it is the most powerful means of achieving a necessary ineffable transformation of reality.Falling in with the herd is an old adage of the modernist avant-garde, which the poets-in-chief believed was a final word that would save humanity from extinction. But these modernists are a far cry from the utopian socialists who believed in the gradual transformation of everyday existence into new, perfect, and truly harmonious realities. There is something naive, even quaint, in this kind of methodical and deliberate tinkerings with the everyday, as if Rabinowitzs experiments simply succeeded in telling us the truth about reality—in a kind of naive and naive-looking verité.In the past, Rabinowitz helped her fellow New York City artists perform with the New York Public Theater. In 1978, a year after the success of the New York City performance of this campaign, Rabinowitz opened an institute for experimental psychology in her own home. An exhibition of her body, found in a closet, her drawings, photographs, and text-based compositions of the time are a kind of visual encyclopedia of mental states and reactions.

Result #2

Literature Review: Predicting the Stock Market Using Machine Learning and Sentiment Analysis for Future Coins," which accompanied the show, proclaimed, in the catalogue, that expert forecasting, far from being a newfangled science, has for millennia had deep roots in art. By how much does art serve the logic of finance and politics? How much is art historical memory? The question posed in this concluding section of the show was already tautological: The discourse on art history is not a new discipline but an evolving one, and art historys current relevance is more than a reflection of its status as a new discipline but is a necessary and sufficient precondition for the persistence of an art today. The question raised here is how art history is informed by the art of today and how art can be useful for the future.An exhibition that carried echoes of the early-twentieth-century art history tradition was called Art as Relation to Science and Technology. It was organized by Giorgio del Fiore and the Italiano Nosti: Art as Relation to Art and Technology and by the Trine de Nemi. The shows focus, as the catalogue stated, was on scientific and technological studies of the human species and also on the relations between human and machine consciousness. The catalogue was based on a five-part series of essays by Hermann Muller (1823–1884) and his student Karl Beckmann (1889–1893) that published in the journal Statik der Natur in 1900. (Mostly for this reason, the work of the psychologists, philosophers, and historians here is less than intellectualistic.) The essays, of which the most important is a map of the Human Minds main physical regions, lay out the specific research on this theme. However, many of the studies in the exhibition were not conceived as machine-readable statistical data but were developed in experiments with human subjects, as are those of the philosopher and physician Hermann Muller (1823–1884).

Result #3

Literature Review: Predicting the Stock Market Using Machine Learning and Sentiment Analysis, 1996, a critique of its single-minded pursuit of the market, was one of its first booklets. In such ways, it was like an encyclopedia of information, an index of the spirit that animates the markets and political actions that determine its activity. Lee Strobel, the publisher of the catalogue, is a close friend of the artist, and some of his images were exhibited in the catalogue. Through this association, we were able to better understand the status of the mind that made the market as an active place. This is a quality of mind that continues to be the basis of contemporary thinking and the critical lens on which it is applied.Strobels efforts to reconstruct the mind of the market were aimed at better understanding how markets operate, as well as at the functioning of the mind, which can also produce very different effects. His aim was to synthesize the thinking that develops in the mind of the traders and economists with the more basic economic and political processes of the mind. Yet even in the most basic of the formal aspects of the mind, there is a spirit of concern for the general public, and for the social world, and of the society that is contained in the mind of a maker and his or her craft. For example, a critical sense of the powers of the mind allows the mind to change; it can undergo radical revisions. Strobel tested his theory on a small group of people who were united by a vision. They were tested on their minds, and the results, he found, were quite instructive. One of the traders, a middle-aged man in a leather jacket, had studied the markets and symbols of the moment, and for several months, he had considered how the art of the moment might be used to create something special for the market. Some months later, he created a live cardboard sculpture in which a replica of a capsule he had found floating in the sea was placed inside it.

Result #4

Literature Review: Predicting the Stock Market Using Machine Learning and Sentiment Analysis, 2016, a semiabstract work in which text is extracted from news headlines, coded, and analyzed by computer, was also on view at the Museum of Contemporary Art, Los Angeles, where the machines behind the predictive algorithms are undergoing further scientific studies.This exhibition was cofounded by Matt Young, a longtime friend of Kim Casteels, a research scientist at Google, and Karen Rosen, a professor in the department of art and digital arts at Princeton University. The organization is already using machine-learning techniques to transform previously unstructured data. But the show also highlights the power of using computer science and data science to make connections between everyday objects and more speculative ones. For instance, the title of the exhibition, whose title comes from the paintings in this show, Adjacent, conjures a tantalizing paradox: The number two is often thought of as an antinominal, implying that it cannot communicate with the two below, and being therefore inaccessible. While it would be hard to imagine a human being completely capable of computing the equivalent of the two, the New York Times released a cleverly coded series of graphics that enables us to draw the two onto a screen. The next chart, for instance, shows the distribution of news stories on a single day, and shows how the proximity of the two bars in the same graph gives the computers score for that day. The same trick applies to texts. The question of which order the words of the alphabet take (for example, the letters A, A, or A, A, or A, or A, or A, or A) is also at work in the computer-graphic array Adjacent, and it is these patterns of words that determine the order in the text—or, to use an analogy for the most important link between art and life, the text and the text.These connections between human and machine are also present in the online world.

Result #5

Literature Review: Predicting the Stock Market Using Machine Learning and Sentiment Analysis, then The Genius Who Knew When, by Donald Baechler, goes on to muse that the best way to predict the market is to look at what is happening in the stock market, via statistical methods. Of course, the works in this show too are informed by the same analytical strategies, just as the stock market is an art object, but the response to their practices is far from uniform. (Is the genius who knew when supposed to predict the stock market?)Others, including some of the artists, have been less successful at predicting stock prices. Erland Foldeck, for example, manages to predict the price of a single, but only once, using data from the Internet. (And, as you might guess, the results were just as accurate.)There is a difference, then, between predicting the market using scientific methods and using machine learning and sentiment analysis. Both can be done with great accuracy, and the trickiness of either is usually the result of the artists retraining. But for many of the artists, machines did more than just enrich the art world. Richard Prince, for example, had a fascination with numbers that helped him discover the ways of predicting stock prices and other phenomena in the market. (Using a very different set of skills than that of art, Prince created a computer program that showed the best matches between stock prices and technical data.) In a number of his pieces, the artists use text or an algorithm to generate a complex feed-forward structure that allows him to reread data and make predictions. But as the data shown in the accompanying computer printout shows, a text message cannot tell you when something is going to happen, even if it can give you a sense of its meaning.And that is one of the advantages of digital technologies: Even if you could decipher the text in the printed word, it would never be able to comprehend the data. The differences are stark: The possibilities of computers, of visual programming, are clearly expressed.