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Tokenization and tomorrow's global economy
- Tokenization 101 – how will the ability to connect, share and create value globally reinvent the financial world?
- Preparing for disruption – how can legacy financial institutions prepare for the disruptive fallout caused by bringing physical assets to the digital world?
- Making it a reality – how can stakeholders come together to implement this vision of an open, inclusive and empowering global economy?
AI & machine learning for trading and investing – what new tools, strategies and products are on the horizon?
- Machine learning models – supervised learning, unsupervised learning, reinforcement learning
- Smart data & real-time indicators – have technology providers solved the challenge of enabling the real-time delivery of accurate and relevant macroeconomic indicators?
- AI & machine learning solutions for emerging data sources – using the latest advances in cognitive computing, deep learning and neural networking to extract unique insights from unstructured and alternative data sources
(Ex)change in stripes – how are market operators harnessing innovation and reinventing business models to respond to modern customer demands?
- Changing business models – strategies for alternative revenue generation
- Vision for blockchain/distributed ledger technology – what is its potential impact on market infrastructure and the current processes for trading, clearing and settlement?
- Crypto trading infrastructure – architecting trading platforms for institutional crypto products
- Cloud factor – impact of moving venue operations to cloud-based systems
- Security & compliance – innovation and initiatives for bolstering security in an increasingly unsecure world
- Artificial intelligence – what role will smart machines play in the exchange of tomorrow?
Alternative data & cryptocurrency trading – a match made in heaven?
- Traditional vs. crypto markets - does alternative data have more of an impact on crypto prices than other asset classes?
- Lack of historical data – how can alternative data fill the void left by the lack of trading history in crypto markets?
- Beyond the Twitterverse – where else are traders finding valuable sources of edgeworthy crypto-specific alternative data?
Workflow integration – how can discretionary managers incorporate new datasets into their trading processes?
- Identification & acquisition – licensing new data sets through vendors or third parties; aligning your data with your investment strategy
- Normalization – storing, structuring and pre-processing data for analysis
- Modeling – backtesting, visualization, machine learning algorithm development
- Signal implementation – automated vs. manual execution
Using AI to enhance asset allocation strategy in the chief investment office
- Exploring the successful and unsuccessful techniques used by AI-driven asset managers
- What are the key bottlenecks in training and inference?
- Which software frameworks and which hardware platforms have proven most useful for those workloads?
- What does deployment look like?
- What are the scaling challenges and the key drivers of the cost?
Blockchain, crypto & the future of money – how will decentralized platforms change the cross-border flow of currency and commerce?
- Industry trends & business drivers – evolving customer needs, aging core infrastructure, innovation across payment products and services
- Opportunities for banks – improved operational efficiencies, increased digitization, parlaying enhanced infrastructure into a platform for additional innovation across businesses and product lines
- The promise of blockchain/DLT – the ideal design for addressing concerns around trust, lack of transparency, cross-border complexities?
- Payments 2.0 – how can a blockchain-based network reduce settlement time and lower costs for international payments?
Quantum computing – the blockchain killer?
Automated Trading & HPC
- Quantum 101 – particle/wave duality, the significance of the double-slit experiment, Heisenberg Uncertainty Principle and mind-boggling paradox at the center of quantum mechanics
- When & how – how vulnerable is Bitcoin and other blockchain-based digital assets to a quantum attack? When is quantum technology expected to reach this capability?
- Preventative measures & post-quantum cryptography – what can the industry do now to mitigate the potential negative consequences of quantum technology on blockchain security?
Fundamental acceleration – using machine learning techniques to forecast fundamental values
- Stale valuation – evaluating timeliness of fundamental information
- Forecasting – how to use a machine learning lasso technique to predict fundamental values
- Results – how does using this technique impact risk-adjusted returns and capital allocation across existing quantitative signals?
Next-generation network connectivity – how are you balancing latency and bandwidth in 2019?
Automated Trading & HPC
- LEO satellites – how is space commercialization impacting data connectivity for low-latency trading?
- Impact of changing marketplace – how are new technological developments and a changing regulatory environment impacting firms’ approach to connectivity?
- New world of alpha discovery – how are firms balancing speed and bandwidth as the algorithmic trading arms race turns toward ingesting and analyzing increasing volumes and varieties of real-time data?
- Network monitoring – solutions, methods and technologies for identifying trends, detecting outliers and correcting excessive latency and jitter in real time
- Risks around building ULL systems – moving data centers, cost of data, exchanges changing the rules
- Trading venues moving to the cloud – realistic vs. deterministic
Alternative data infrastructure – aligning technology, strategy and human capital to achieve peak investment performance
- Outsourcing vs. internalizing – how to decide whether to fully outsource, partially outsource of fully internalize big data infrastructure
- Compute & storage resources – how to determine compute and storage requirements
- Machine learning & analysis tools – visualization software, open-source libraries
- Human capital dynamics – how to assemble the most effective team to execute your alternative data strategy
Enterprise data management – how are you managing, governing and integrating data to achieve peak performance across applications, systems and services?
- Warehousing & integration – best practices for building an enterprise data warehouse and unifying data management across different IT teams and departments
- Governance – how to design an effective data governance strategy for managing regulatory risk and staying compliant with LEI, MiFID II, GDPR and other reporting requirements
- Roadblocks – data quality, integrating new technologies with legacy infrastructure
- Cybersecurity – what are the most effective solutions for securing high-performance networks and protecting sensitive enterprise data against evolving threats?
- Standardization & open data – what are the benefits and drawbacks of implementing a common financial industry language, or financial Rosetta Stone, on operational efficiency and regulatory reporting?
- Intelligent storage – how to store and protect data needed for emerging AI and real-time analytics applications
Token gesture – a framework for assessing the value of crypto tokens and alt coins
- Factors driving the value of crypto assets – the network effect, low correlation to traditional assets and the rise of digital monetary commodities
- Traditional valuation vs. crypto valuation – why traditional models of valuation don’t make sense for crypto assets; why equity bubbles are a false analogy for the rapid growth in Bitcoin
- Economic impact – how crypto assets will change the dynamics of investing much in the same way the Internet changed the flow of information
- Valuing Bitcoin’s dominance – could another alt coin displace Bitcoin; is it worth diversifying your portfolio with other crypto assets?
- Crypto metrics & market data – what types of datasets are most useful in determining value?
Smart execution and the alpha of trade performance – how to harness real-time TCA to lower execution costs, optimize trading algorithms and predict market behavior
Automated Trading & HPC
- Less alpha, tighter spreads, thinner margins & increased risk aversion – how must quant funds, trading firms, asset managers and banks adjust when implementing a market data strategy?
- Performance measurement solutions – post-trade, intra-day and real-time cost analysis
- Capturing, cleansing, storing & analyzing market data – how to enable alpha discovery across asset classes and geographies in turbulent market conditions
- Vendor relationships – the build vs. buy puzzle and working with your provider to improve performance, capacity and cost efficiency
- Real-time insights – how can traders use visualization technology to aid in real-time pattern discovery and outlier detection?
- Real-time pattern detection – how to use low-latency, complex event processing (CEP) technology to interpret live data streams
Alternative data – how are you leveraging nascent data sources for alpha generation?
- Datafeeds – organizing and integrating alternative data for benchmarking, performance measurement, quantitative analysis, application development and trade execution
- Exclusivity vs. processability– is one more important than the other when mining for alpha?
- Speed vs. veracity – how are vendors balancing performance and accuracy in the sub-millisecond delivery of real-time, ultra-low latent machine-readable news and economic data?
- Alpha decay & packaged alpha products – how are vendors accounting for crowding and the resultant decay of the value with time?
Re-thinking risk – how are savvy funds machine learning technology to augment risk management?
- Complexity & volatility – how are portfolio managers accurately assessing risk in unpredictable modern market conditions?
- Next-generation data management & analytics – leveraging new tools to achieve an integrated view of portfolio risk exposure across time horizons and asset classes
- Risk modeling – how can new validation tools be applied to ensure accuracy?
- Predictive analytics – how are you using predictive tools to measure risk?
- Compute-intensive risk – how are quants and risk managers using the cloud to scale up modeling capacity without breaking the bank on building new data centers?
- Alternative data for risk management – how can “alt” data be leveraged as an indicator for measuring risk, anticipating volatility and extracting unique market insights?
Custodianship and asset security in digital currency markets – the last hurdle to crypto legitimization?
- How is the maturing cryptocurrency economy addressing the issue of reliable custodianship in digital currency markets?
- How much institutional money is still parked on the sidelines due to a lack of reliable asset security?
- How will the introduction of digital currency custodians serving institutional investors impact the crypto economy?
- Wallets vs. compliant custodians – crossing the chasm
- What are the unique security risks involved with irreversible transactions?
From fundamental to quantamental – how are discretionary managers using machine learning and big data technologies to enhance investment decision making?
- Data integration challenges – how are fundamental investors overcoming the technical and corporate barriers to integrating big data into front-office systems?
- Dissecting quantamental investing & the human element – how can new technologies like machine learning be effectively integrated into a fundamental investment process? What can systematic managers learn from discretionary managers, and visa versa?
- Human capital evolution – how have talent and skill sets changed over time in the discretionary world? Is business and economics acumen no longer as important as engineering and computer science skills?
Exploring the D’s of disruption – democratization, disintermediation, datafication and decentralization
- Democratization of investment research – does crowdsourcing earnings estimates from anonymous contributors, professionals and non-professionals from the buy-side, sell-side and academia lead to more accurate forecasting than traditional sell-side research?
- Data democracy – is the trend toward equal access to information eroding alpha? If so, how can active managers pivot to regain a competitive edge?
- Real-time information – how are social networks, blogs and other digital mediums offering instantaneous access to news and information filling a void left by traditional investment research?
- Overcoming blockchain’s kryptonite – what are the most promising solutions to the scalability problem posed by the decentralized structure of distributed ledgers?
- Canary in the crypto mine – how can technologists, policymakers and capital markets leaders address the ever-increasing electricity/compute/e¬nergy resources being consumed by proof-of-work mining operations?
- Mining centralization – are you concerned about a potential hidden power structured created by industrial mining pools driving network centralization?
last published: 13/Jun/18 18:35 GMT