How Satellite Imagery is Revolutionizing the Way we Invest

It is the night before a shareholder meeting in Eastern Africa to vote on a major decision, whether or not to invest in 100 acres of land for agriculture. Your assignment is to persuade a group of stakeholders to buy in on a $56M investment, until you are confronted with a critical decision that could determine your career. A data analytics partner sends classified geospatial insight to your device stating that Ethiopia has suddenly had its worst harvest and crop yield in over a decade with undeniable evidence of forecasted improvement, contradicting your existing data. While the rest of the team is asleep one supervisor in London who received the same notification orders you to follow through with the original proposal. After hours of contemplating and drafting a plan without any sleep, the following day you deliver a pitch to allocate funds towards an analogous project.

The consensus of the entire room is an overwhelming affirmation.

What would have happened if the insight was never sent? And what if there was a way to bring greater transparency to investors before executing decisions and before markets are impacted?

A Technical Glimpse into a Back End Solution from Space

Amidst the data revolution, strategic investment teams are constantly searching for alternative datasets to stay ahead of the game. A key component of successful investing practices is to gather a well-informed outlook of the situation at any given time. One unique solution to capitalize in the financial markets is from the underlying information within satellite imagery. In particular, by applying deep learning algorithms to imagery, we can analyze trends and anomalies over an area of interest. As objects move and patterns arise on the ground, imaging satellites are capturing snapshots of activity on planet Earth with resolution as high as 30 centimeters per pixel in real time! Analysts, software engineers, and developers are training datasets with convolutional neural networks, feature and anomaly detection algorithms, and other applicable computation to extract and deliver critical insight. A vast number of companies from finance to data analytics are fine tuning predictive models from evaluating trends across petabytes of historical remote sensing databases over time periods from as far back as decades ago.

A Resolution Transforming Investor Insight

Imaging satellites are reshaping how traders invest in stocks, commodities, and real assets. Although it may bring more immediate value to commodity markets, hidden information concealed beneath layers of satellite imagery supports a wide range of trading and investment activities. Asset management companies, stock brokers, and investment advisory firms may receive time-sensitive business intelligence long before markets are affected. Utilizing powerful geospatial datasets, strategic investment teams can gain competitive advantage from events as they unfold anywhere in the world. Satellite derived intelligence integrated with external datasets into predictive investment models can reduce beta measurements, yielding higher risk tolerance and alpha levels, or forecasted confidence intervals. Timely, affordable Earth observation data is enhancing the accuracy of financial derivative instruments, future options, capital asset pricing models, and other estimation tools.

As existing forecasting models can be unreliable, it can be difficult for producers and manufacturers to hedge risk by accurately predicting future prices. By monitoring supply chains, inventory levels, and production activity, Earth observation-based data sets help entities better forecast supply, demand, and profit zone levels. Commercial users and speculators mobilizing geospatial datasets can capitalize on futures contracts. For example, RS Metric’s, a New York firm which sells imagery analytics for investors, provides a CME Group Copper Futures Predictive Analysis with price accuracy levels greater than 75% as far as three months in advance of future prices.

The most critical times to monitor a commodity or stock are in times of dynamic price variation and before/during/after major events; which are critical monitoring periods to maximize return on investment. Hedge fund managers and decision makers focus on the short-term, speculating whether or not a company will meet quarterly earnings reports. With the addition of Earth observation data products, investors are able to focus on the very short-term information with a more resolute understanding.

Establishing mitigation tactics and optimal exit strategies before supply chain disruption or an external event influencing the market is becoming common practice. Competitive advantage is becoming based on how far in advance predictive models can forecast market trends while maintaining accuracy. Engaging more precise forecasting will help determine whether or not portfolio accounts will meet production goals, shareholder expectations, and quarterly earnings reports. Although a riskier maneuver, betting against the market with satellite intelligence is still less prevalent.

When investing in infrastructure and construction projects, satellites are a powerful assessment tool to monitor the social, environmental, and financial impact of investments. It could be one of the most influential methods to monitor foreign direct investment (FDI) in remote regions of the planet.

Wealth management, investment communities, and data analytics organizations can task their own satellites to monitor portfolio assets and asset classes around the clock. As a cooperation of satellite owners, the unification of partnerships in the financial industry may yield greater tailored capability with more affordable price points, higher spatial resolution and shortened revisit rates.

This satellite integrated system can derive both fundamental micro and macro trends. It may open doors to new understandings further explaining stock or commodity volatility.

Use Cases Applied to Notional Scenarios

One example we’ll examine is the life of a soybean. A soybean sprouts out of the ground in northern Brazil, harvested with a record-breaking national yield, piloted with a multimillion-dollar soybean order in transit on a 17,000 km journey to Thailand, is hijacked by eastern African pirates in GPS dead zones, and delivered to feed thousands of animals in Kenya.

The dissemination of such crucial information can be held internally until released to the public, if it weren’t for hundreds of imaging satellites orbiting Earth. Investors in local soybean commodity exchanges who are monitoring the soybean shipment with remote sensing analytics will be able to take action before market impact.

Another more conventional example is in the natural resource industry in middle eastern markets. As the fourth largest gold consumer, the UAE was exploring for gold mines in Yemen one decade ago. Thani Dubai Mining Company identified over 80 wells and extracted over 8,000 rock samples in Yemen before retail gold demand volumes dropped by 15 percent the same year. Major stakeholders in the Dubai Gold & Commodities Exchange made the right investment decisions after receiving paramount mineral extraction activity from satellite imagery before it was released to the public and distributed to local markets.

Spearheading the Geospatial Predictive Analytics Movement

Commercial satellite imagery providers such as DigitalGlobe, Planet Labs, Image Sat International, and UrtheCast are pioneering a movement towards the back-end value chain of the commercial segment of the Earth observation industry. Companies in the value-added service market, Orbital Insight, Ursa Space, Clipper Data, RS Metrics, and SpaceKnow, are shedding light on tightly held secrets in the world of commodity trading and investing.

From analyzing historical imagery databases of cars across national branches, companies can identify correlations between counting cars, operational trends, and in-store purchases.

Orbital Insight monitors fundamental consumer traffic data covering 260,000 retail locations, 3,300 high demand U.S. shopping centers, 25,000 oil storage tanks across 1,000 industrial facilities and several other fields such as Real Estate, Financial Services, Commodity markets, Commercial Airports, and the Insurance industry. During the two outbreaks of E. Coli at the Chipotle restaurant chain in 2015, Orbital observed a sharp decline in car traffic which was an invaluable precursor for a 40% decline in the stock price.

In the Oil and Energy industry, Ursa Space monitors over 3.6 billion barrels of oil, 10,000 oil tanks, and 340 terminals around the world on a weekly basis. Clients can leverage imagery data products and incorporate custom applications such as offshore oil rig monitoring, transportation tracking, bulk commodity observation, infrastructure, construction and maintenance into their business models.

A New York based firm, Clipper Data provides intelligence and analysis in the Oil and Energy industry, as well as comprehensive shipping information for investor purposes. With the combination of several geospatial datasets, Clipper provides a robust platform and database on global cargoes of crude oil, refined products, and petrochemicals.

Astute institutional shareholders of Tesla observed higher returns after receiving cutting edge-insight from RS Metrics. In June of 2018, the company informed clients of Tesla’s new general assembly structure, car manufacturing activity, and output production rates, three days prior to it becoming public knowledge. Additionally, RS Metrics has three other predictive investor products, in the Traffic, Real Estate, and Metal and Commodities Industry. Satellites detecting off-site warrant facilities, manufacturing activity, and global metal production moving in and out of physical market “shadows” afford as high as one to three month ahead directional price and inventory forecasts.

Another stellar example is Silicon Valley-based startup Spaceknowthat compiles a China Satellite Manufacturing Index by analyzing images of buildings, roads, factories and construction activitywith scoring models to designate values across 6,000 industrial areas in China.

Exchange-Traded Funds (ETFs), public firms and investment institutes on wall street are experiencing that satellite imagery is becoming one of the dominant sources of alternative data. As enhanced temporal and spatial imagery resolution becomes commercially available worldwide, geospatial datasets may become much more prevalent across all segments of the investment industry.

A Method to Evaluate Opportunity for Insight with Imagery

As markets are incredibly complex systems, there are an abundance of observable components within imagery to factor into the equation. It is important that organizations, and governments use optimal performance metrics to better forecast investment decisions.

Near real-time insight from applying geospatial detection features to the outlined industry applications, is enabling hedge funds, investment banks, and commodity traders to better estimate when to buy and sell. Integrating a myriad of perspectives while analyzing imagery such as alternative datasets from IoT devices, social media, people-counting, payment transaction data, and internal financial trends is significantly enhancing decision making.

The Geospatial Industry applications outline a glimpse of the market activity apparent with imaging satellites that teams may consider to assimilate into investment models.

Firms may evaluate satellite analytics cost analysis reports to identify optimal routes of geospatial integration. As the movement progresses, auditing the decisions and operations of competition, with satellites may become an increasingly controversial tactic to gain market share. With hundreds of imaging satellites orbiting Earth every day, companies can employ machine learning to petabytes of imagery databases dating as far back as 15 year ago to shape market trends.

Consistencies in satellite imagery that are contributing to a loss of time and money are aggregated and computed into investment models to construct performance studies. Time-cost saving algorithms and cost-benefit analysis can be significantly improved to better estimate the value against costs of a single decision, project, or policy. As high resolution imagery becomes more ubiquitous, we can better examine the financial and environmental impact of political decisions from economic assessments across each industry.

As the greenest form of industrial transportation, shipping accounts for 90% of world trade. A majority of shipped goods comprise a larger portion of the commodities markets. Feature and trend detection algorithms can track shipping activity, container content and transportation, and end to end commodity supply chains. One could theorize that the activity and trading decisions of over 90% of commodities may be able to be improved from derived satellite imagery data. Value for traders and investors may be from improved accuracy of forecasting models and merging diverse datasets to assemble a well-informed outlook. Automated, customizable, periodic, pattern of life analysis may yield significant competitive advantage into commodity trading and stock markets.

Shareholders, traders, and investors can monitor geospatial detection features across a multitude of commodity markets outlined below. National, regional and global geospatial commodity indexes may incorporate suitable detection features and industry applications to create sharp priority scoring models, bolstering portfolio performance.

Investment management software such as Personal Capital, eSignaland OpenLink may consolidate geospatial datasets to enhance trading activity. Value added services such as Commodity Trade and Risk Management (CTRM), Portfolio Fund Management and Tracking, and Stock Analysis software may integrate imaging-based intelligence into their computational models to harness alpha-generating insights.

It is also beneficial to analyze portfolio performance differences after making decisions arising from satellite imagery. Geospatial cost-saving reports may help calculate potential losses on investment and cumulative impact of less preferred financial scenarios to better determine the value of end user insight. Utilizing sensitive information such as the value of geospatial data over a period of time may enhance communication with partners and help satellite owners and imagery providers better assess time, capacity and financial values of operations, as well as consumer and product price points.

Consumer segment or product price points may be valued as an agreed upon percentage of the value of information received. For example, if a nonrecurring packet of information saves a portfolio or company from bankruptcy, it may be more fair for both parties to have predefined a percentage of financial loss after agreeing upon a cost-saving analysis.

Governments and companies can derive local, regional, and national investment analysis reports from a unique combination of detection features and applications. Hundreds of geospatial variables and alternative datasets can be computed to foresee investment success and attraction levels scalable across cities and countries.

With hundreds of imaging satellites expected to launch within the next few years, humans may find many more underlying trends that may be crucial from an investors viewpoint.

A Glimpse into the Future of a Changing World

Geospatial datasets provide a reliable perspective to help us make better decisions. With reduced costs to design, integrate and own a satellite, coupled with diminishing barriers to launch spacecraft into orbit, the Earth Observation market will be capable of producing ultra-sharp point imagery bordering the regulation of 25 cm resolution, with dwindling, revisit rates of a fraction of an hour.

This may one day establish the foundation for satellite manufacturing Indexes (SMI), space based intelligence commodity indexes and geospatial exchange indexes, comprised of a set of financial derivatives that may one day transform the way we do business.

It may be advantageous to surmise that the investing industry is entering into a new predictive analytics era where we may find ourselves asking questions such as, who has superior artificial intelligence?

Elevated awareness may lead to more investment opportunities, reduced risk, and groundbreaking innovations. Enhanced communication between financial industries, geospatial communities, and machine learning scientists may initiate lifelong partnerships to accelerate investment decisions and to improve life on earth.

With this information we can make better decisions to not only save time and money but to make the world a better place.

From the life of a transatlantic soybean to a career ending African investment decision to the depths of the middle eastern gold trade, geospatial insight is revolutionizing how we invest.

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