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Alternative data applies to any dataset that a trader utilizes beyond the standard sources of ‘price, volume, earnings, and so on’. These include social media, satellite images, and many other data sets. With an array of information at their disposal, one of the challenges traders face is sorting through the data that’s relevant to them. In this article, we look at the different kinds of alternative trading data available in the market and how they foster modern day trading.

1. What is Alternative data?

Not to be confused with basic financial data sources, alternative data has emerged to be valuable insight derived from non-conventional data sources. Such data can come from companies and stocks that may be external to the particular entity in question. This point may be a large motivator for many traders as they no longer have to rely solely on financial data that sometimes presents analysis with a significant lag. It enables a more accurate and on-time interpretation of factors such as the economy, market, and consumer sentiment.

Characteristics of Alternative Data:

Volume: The alternative index is usually high in volume and comes in large panels that are mostly unstructured.

Frequency: There is a never-ending flow of new and updated data that is being introduced continuously.

Inclusiveness: Has a broad variety of data types, such as images, written and physical action.

There are modern tools and their availability that have significantly lessened this problem and now makes it possible to process such big data sets to gain insights for forecasting in trading activities.

2. Forms of Alternative Data

There are many forms of alternative data but they can be broadly classified into some categories. Listed below are some categories that are often referred to in the trading context:

A. Social Media and News Sentiment

Social media and News supplement the investment information. Social media, blogs, websites and, newspapers can be integrated to determine whether the stock or the corporation or the market is in good or bad form. Natural language processing is one of the techniques that will be used to perform such tasks.

Applications: Such a trend can serve as an indicator of future changes in prices when US dollar-tied currencies, such as the Swiss franc, are analyzed. News sentiment can also in this case warn traders about forthcoming events that have an ability of altering stock price.

B. Traffic to Websites and Use of Apps

Traffic to e-commerce sites and the number of active users on mobile applications are also measures that determine the company performance, as it helps to understand the level of interest from consumers, and potentially the financial returns that the company will achieve. High web and app engagement for a company indicates high level of interest from consumers which may translate to the company making good profits.

Practices: Increased number of clicks on the retailers site or high use of the app can be indicative of increased revenues in the future, which is also beneficial for traders watching on stock of consumer companies.

C. Geolocation or Place Data

Geolocalization data obtained mostly from mobile devices contain information about the flow of people within physical establishments further for instance, supermarkets, hotels or airports. With a large sample size of devices, aggregated data can reveal and predict consumer behavior.

Practices: Physical shop traffic of consumers for a certain retail line may bear a positive correlation to sales, and thus time series forecasting of such data can provide good indicators of stock prices for that industry.

D. Satellite and Aerial Photography

Overhead scenes or Satellite images can show the sites of physical infrastructure such as oil depot, fields and construction works. Machine learning supports the detection of market images changes through algorithms that assess these images.

Applications: Images captured from satellite surveillance of oil storage tanks can assist traders to gauge the level of oil in stock and help in predicting the price, while images of crop fields may give weather information in relation to agricultural commodities.

E. Weather and Climate Data

The weather conditions have an effect on weather in agriculture, retail and distribution, energy and logistics. Weather data, complimented with the historical financial data, can go a long way in predicting the demand for a particular product or commodity.

Applications: Assisting in predicting crop production for relevant agricultural commodities or predicting fluctuations in energy demand due to changes in the temperature.

F. Transaction and Credit Card Data

Contrary to other methods of collecting information regarding, for instance, company sales, transaction data collected through credit card transactions and anonymized in this manner presents a more direct evidence of consumer spending habits. Understanding large flows on accounts allows us to better understand and assess affected consumer markets and industries — their growth or decline.

Applications: A specific direction or a set of directions receiving high concentration spending may reflect considerable future earnings for companies within that particular sector.

3. The Role of Alternative Data in Trading

In algorithmic trading, alternative data are employed to obtain information or insight into what conventional financial indicators may miss. Hence, if properly processed and explained, such data can result in a better forecast of price behavior and thus enhanced strategies. Here are the ways how alternative data changes the trading game.

A. Greater Predictive Accuracy

With the incorporation of real-time data sources, traders get immediate and finer details rather than looking up traditional financial metrics. For example, consumer transaction data can exhibit direction in spending before the quarterly revenue performance, thus enabling traders to gauge how stocks will perform.

B. Potential Market Changes

Alternative data can assist in the anticipation of potential market changes in the future. For instance, geolocation data may detect a decrease in retail sales foot traffic before sales reports are published, allowing traders to pre-emptively shift their positions.

C. Information Asymmetry

As ordinary traders can only accrue so much information as general intelligence permits, institutional clients regularly leverage exclusive research and insights that are often out of reach for retail traders and investors; making it an imbalance of information in the market. Alternative data can fill the void of information to let more widespread information sources reach a greater audience and equitable the situation.

D. Real Time Analysis in Trading and Change Management

The pace at which alternative data is structured allows traders to keep pace with changes. For example, the use of social networks can help traders respond to information, changes, and other announcements instantly.

4. The Evaluation of Alternative Data Memorised by Operators

Though the data provided by alternative data helps one to see clearly it is not devoid of its difficulties.

A. Authenticity, Accuracy and Completeness of Data Quality

Often, alternative data is not standardized and its verification is often difficult. For example, data based on social networks is often messy and complex which warrants a lot of preprocessing and filtering to make the analysis ready for the conclusion stage.

B. Data Security and Ethical Management Strategies

Alternative data, particularly geolocation and transactional data raises data security issues. Providers of data must be able to address privacy laws like GDPR which will affect the due availability of some datasets.

C. Accessibility and Cheaper Alternative Options

Data which is quality-oriented is pretty expensive and only few traders such as institutional investors or hedge funds are able to have this upper hand. This particular barrier is the reason behind the restricted access of alternative data by individual traders.

5. The Future of Alternative Data in Trading and decision making

The relevance of alternative data will continue to increase as the advances of technology, namely artificial intelligence and machine learning, enable the processing and understanding of bigger and unstructured data. Here are a handful of developments that are likely to influence the future of alternative data.

More Retail Participation: With more alternative data vendors coming into the market alternative data is likely to be available to retail traders.

Better Data Fusion: Incorporation of alternative data into traditional datasets and fusion of various data types will help the construction of more comprehensive and accurate models.

Data Analytics Evolution: There will be availability of new tools and techniques particularly in NLP and image analysis that will allow better interpretation of more sophisticated types of data such as social media narratives and imagery from satellites.

Conclusion.

The trading industry is undergoing a significant transformation owing to alternative data which provides a wider horizon rather than traditional sources of data. This includes insights from social media, sentiment analysis or satellite images which give traders a competitive advantage over the rest by enabling them to predict events and trends better. These developments, however, would need sophisticated data processing capabilities and also concern about privacy. In the future enhanced technologies will continue to strengthen the alternative data and its application in trading will be useful to the firm who is able to leverage this fully.

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